From c69ce8fda827ae849735614f8c5a0a5563948811 Mon Sep 17 00:00:00 2001 From: Travis Driver Date: Sat, 27 Jan 2024 14:44:36 -0500 Subject: [PATCH 1/2] Added CReTA --- .gitignore | 4 + ..._21_20_312296_aggregated_results_table.tsv | 253 +++++++++++ creta_bata.py | 427 ++++++++++++++++++ dummy_data.mat | Bin 0 -> 2763754 bytes .../averaging/translation/averaging_creta.py | 119 +++++ .../translation/translation_averaging_base.py | 4 + gtsfm/configs/unified.yaml | 3 +- gtsfm/multi_view_optimizer.py | 2 + scripts/benchmark_wildcat.sh | 50 +- thirdparty/DKM | 1 + thirdparty/LightGlue | 2 +- thirdparty/creta/BATA.m | 219 +++++++++ thirdparty/creta/BATACost.m | 49 ++ thirdparty/creta/CReTA_demo.asv | 196 ++++++++ thirdparty/creta/CReTA_demo.m | 197 ++++++++ thirdparty/creta/LUDCost.m | 49 ++ thirdparty/creta/RLUD.m | 173 +++++++ thirdparty/creta/ReadMe.txt | 17 + thirdparty/creta/RunTA.asv | 83 ++++ thirdparty/creta/RunTA.m | 83 ++++ 20 files changed, 1903 insertions(+), 28 deletions(-) create mode 100644 2024_01_26_18_21_20_312296_aggregated_results_table.tsv create mode 100644 creta_bata.py create mode 100644 dummy_data.mat create mode 100644 gtsfm/averaging/translation/averaging_creta.py create mode 160000 thirdparty/DKM create mode 100644 thirdparty/creta/BATA.m create mode 100644 thirdparty/creta/BATACost.m create mode 100644 thirdparty/creta/CReTA_demo.asv create mode 100644 thirdparty/creta/CReTA_demo.m create mode 100644 thirdparty/creta/LUDCost.m create mode 100644 thirdparty/creta/RLUD.m create mode 100644 thirdparty/creta/ReadMe.txt create mode 100644 thirdparty/creta/RunTA.asv create mode 100644 thirdparty/creta/RunTA.m diff --git a/.gitignore b/.gitignore index b42ec8ad5..3bf65a12f 100644 --- a/.gitignore +++ b/.gitignore @@ -186,6 +186,10 @@ result_metrics/ # Folder created by hydra output/ +# CReTA inputs/outputs +ta_input* +ta_output* + # Cache dir cache/ diff --git a/2024_01_26_18_21_20_312296_aggregated_results_table.tsv b/2024_01_26_18_21_20_312296_aggregated_results_table.tsv new file mode 100644 index 000000000..0d6f903e7 --- /dev/null +++ b/2024_01_26_18_21_20_312296_aggregated_results_table.tsv @@ -0,0 +1,253 @@ +method_name retriever_num input images retriever_num retrieved image pairs retriever_retriever duration sec isp_rot3 angular errors deg isp_trans angular errors deg isp_pose errors deg isp_total correspondence generation duration sec isp_total two view estimation duration sec vg_num input measurements vg_num inlier measurements vg_num outlier measurements vg_inlier R angular errors deg vg_inlier U angular errors deg ra_rotation angle error deg ra_total duration sec ta_relative translation angle error deg ta_translation angle error deg ta_total duration sec ta_outlier rejection duration sec ta_optimization duration sec da_triangulation runtime sec da_gtsfm data creation runtime da_total duration sec ba_number cameras ba_number tracks filtered ba_3d track lengths filtered ba_reprojection errors filtered px ba_rotation angle error deg ba_relative translation angle error deg ba_translation angle error deg ba_pose auc @1 deg ba_pose auc @2.5 deg ba_pose auc @5 deg ba_pose auc @10 deg ba_pose auc @20 deg ba_step 0 run duration sec ba_step 1 run duration sec ba_step 2 run duration sec ba_total run duration sec total_total runtime sec +20240125_175029__courtyard__results__num_matched10__maxframelookahead0__760p__unified_disk 38 177 16.799 0.1 / 7.7 0.3 / 11.5 0.4 / 12.7 16.3055 139.949 152 121 31 0.1 / 0.3 0.3 / 0.7 0.2 / 0.2 0.00403285 0.5 / 0.8 0.3 / 0.3 1.03516 0.980202 0.00713396 13.3884 2.45313 15.8415 16 7207 3.0 / 4.0 0.5 / 0.6 0.1 / 0.1 0.3 / 0.4 0.1 / 0.1 37.0804 40.0953 41.1003 41.6028 41.854 2.61888 4.25353 3.24926 10.1218 233.136 +20240125_175029__courtyard__results__num_matched10__maxframelookahead0__760p__unified_lightglue 38 177 40.5206 0.2 / 11.4 0.7 / 19.0 0.8 / 22.2 17.561 95.794 160 127 33 0.2 / 2.6 0.5 / 9.7 2.6 / 3.1 0.0455034 0.6 / 1.6 18.6 / 18.6 3.16699 3.0721 0.0428405 16.1998 2.28949 18.4893 20 3481 3.0 / 3.8 0.5 / 0.6 31.4 / 40.7 2.0 / 18.3 5.1 / 11.6 0 0 0 0 0 8.18789 7.04326 8.46298 23.6943 230.814 +20240125_175029__courtyard__results__num_matched10__maxframelookahead0__760p__unified_loftr 38 177 16.8946 0.2 / 2.6 0.7 / 9.9 0.7 / 10.0 16.082 23.6897 124 97 27 0.2 / 0.5 0.6 / 1.9 0.1 / 0.1 0.00915408 0.2 / 0.4 1.0 / 1.0 1.2472 1.16693 0.0414436 11.0589 2.25626 13.3152 13 6035 3.0 / 3.9 0.4 / 0.5 0.3 / 0.5 0.1 / 1.1 0.3 / 0.6 17.9633 26.003 30.2503 32.2304 33.2205 0.955439 0.967584 0.972927 2.89605 103.458 +20240125_175029__courtyard__results__num_matched10__maxframelookahead0__760p__unified_sift 38 177 19.0535 0.4 / 1.6 1.3 / 3.6 1.3 / 3.8 9.37661 20.0952 122 111 11 0.4 / 0.7 1.3 / 2.8 0.3 / 0.3 0.00641274 0.8 / 1.1 0.3 / 0.5 0.723441 0.690486 0.0163436 4.96277 0.718443 5.68121 12 2000 4.0 / 4.7 0.2 / 0.2 0.0 / 0.1 0.1 / 0.1 0.0 / 0.0 29.8612 30.8919 31.2354 31.4072 31.4931 0.33436 0.329968 0.323768 0.988191 82.4971 +20240125_175029__courtyard__results__num_matched10__maxframelookahead10__760p__unified_lightglue 38 347 10.5739 0.6 / 22.7 1.4 / 27.0 1.5 / 34.7 13.4743 95.392 238 154 84 0.2 / 5.2 0.7 / 14.5 33.6 / 37.0 0.0239654 1.2 / 11.9 20.0 / 20.1 3.42466 3.35641 0.0293913 10.6733 1.86331 12.5366 37 5292 3.0 / 3.9 0.5 / 0.6 35.5 / 33.8 46.6 / 53.2 20.6 / 20.1 0 0 0 0 0 7.15319 7.25209 7.13162 21.537 187.555 +20240125_175029__courtyard__results__num_matched10__maxframelookahead10__760p__unified_sift 38 347 10.6197 0.4 / 2.2 1.4 / 4.6 1.5 / 4.7 8.92529 23.5293 144 132 12 0.4 / 0.8 1.3 / 3.1 0.5 / 0.7 0.0148835 1.3 / 2.5 0.6 / 1.0 2.0297 1.98749 0.0211282 5.44046 0.865744 6.3062 29 2257 4.0 / 4.4 0.2 / 0.2 0.4 / 0.5 0.9 / 1.6 0.6 / 1.0 29.0928 49.2619 61.4621 68.889 72.6024 0.584622 0.58336 0.574964 1.74304 80.2937 +20240125_175029__courtyard__results__num_matched10__maxframelookahead10__760p__unified_superglue 38 347 10.826 0.6 / 25.5 1.5 / 29.2 1.6 / 37.3 13.4741 89.4689 242 150 92 0.2 / 8.9 0.7 / 14.4 32.8 / 35.4 0.726521 0.7 / 16.2 19.5 / 22.1 3.24699 3.15549 0.0558207 10.1594 1.8202 11.9796 24 3384 4.0 / 4.1 0.5 / 0.6 47.2 / 47.2 29.6 / 52.8 21.9 / 21.7 0 0 0 0 0 2.93659 2.94453 2.94185 8.82319 166.824 +20240125_175029__courtyard__results__num_matched5__maxframelookahead0__760p__unified_disk 38 127 10.8703 0.1 / 7.7 0.3 / 10.7 0.3 / 12.4 32.4983 112.839 110 84 26 0.1 / 1.2 0.2 / 2.2 0.2 / 0.2 0.0146129 0.3 / 0.5 12.6 / 14.4 1.62162 1.53078 0.023874 26.4546 4.83255 31.2872 14 6794 3.0 / 3.8 0.4 / 0.5 0.1 / 0.1 0.2 / 0.3 0.1 / 0.1 33.1253 35.3554 36.0987 36.4704 36.6563 1.88441 1.87139 1.83631 5.59222 225.916 +20240125_175029__courtyard__results__num_matched5__maxframelookahead0__760p__unified_lightglue 38 127 10.9922 0.2 / 10.6 0.6 / 14.7 0.6 / 18.2 24.379 67.0986 116 95 21 0.1 / 5.1 0.4 / 8.9 0.8 / 3.5 9.49159 0.5 / 7.9 10.9 / 13.1 2.21833 2.13004 0.0482392 10.7738 1.92565 12.6994 20 3459 3.0 / 3.6 0.5 / 0.6 13.9 / 21.6 2.4 / 17.9 5.5 / 5.9 0 0 0 0 17.2341 4.09008 4.10629 4.11061 12.3071 166.969 +20240125_175029__courtyard__results__num_matched5__maxframelookahead0__760p__unified_loftr 38 127 10.9194 0.2 / 1.7 0.6 / 6.8 0.6 / 6.8 39.7084 21.3809 95 79 16 0.1 / 0.5 0.6 / 3.3 0.1 / 0.2 0.00613308 0.3 / 0.3 0.1 / 0.1 0.813826 0.77169 0.00712824 10.2037 2.22239 12.4261 17 6140 3.0 / 3.8 0.4 / 0.5 0.1 / 0.1 0.2 / 0.3 0.1 / 0.1 40.843 43.1793 43.9581 44.3475 44.5421 1.06389 1.01734 1.03322 3.11456 117.812 +20240125_175029__courtyard__results__num_matched5__maxframelookahead0__760p__unified_sift 38 127 15.2417 0.3 / 1.7 1.4 / 3.9 1.4 / 4.1 19.8886 17.6372 95 83 12 0.3 / 0.6 1.3 / 2.9 0.3 / 0.3 0.00452876 1.0 / 1.2 2.6 / 3.1 0.654867 0.631101 0.00857425 4.89289 0.690511 5.5834 11 1904 4.0 / 4.7 0.1 / 0.2 0.0 / 0.1 0.1 / 0.1 0.0 / 0.0 27.3757 28.3187 28.633 28.7902 28.8688 0.334405 0.323203 0.320026 0.977739 86.2342 +20240125_175029__courtyard__results__num_matched5__maxframelookahead0__760p__unified_superglue 38 127 10.8165 0.1 / 10.6 0.6 / 14.0 0.6 / 17.1 39.8508 66.6161 114 86 28 0.1 / 3.6 0.4 / 9.8 1.2 / 1.4 0.00797677 0.2 / 25.7 23.4 / 21.9 0.990059 0.936938 0.0253842 9.81733 1.65035 11.4677 11 3614 4.0 / 5.4 0.4 / 0.5 0.0 / 0.1 0.1 / 0.1 0.0 / 0.0 27.5041 28.37 28.6587 28.803 28.8752 1.1404 1.13945 1.15553 3.43548 162.177 +20240125_175029__courtyard__results__num_matched5__maxframelookahead10__760p__unified_lightglue 38 336 10.6315 0.6 / 22.2 1.4 / 26.8 1.4 / 34.2 21.2324 90.8371 231 148 83 0.2 / 4.8 0.6 / 14.0 36.4 / 41.9 0.0190368 0.6 / 11.6 21.3 / 20.8 3.31223 3.24749 0.0278387 11.1501 1.95025 13.1004 13 2428 3.0 / 3.9 0.6 / 0.7 4.8 / 8.8 0.5 / 17.7 3.7 / 4.3 0 0 2.32658 15.9173 23.7481 7.26142 6.94904 6.90472 21.1153 188.861 +20240125_175029__courtyard__results__num_matched5__maxframelookahead10__760p__unified_sift 38 336 10.7383 0.4 / 2.3 1.5 / 4.7 1.5 / 4.8 17.3318 23.1414 139 127 12 0.4 / 0.8 1.3 / 3.1 0.5 / 0.7 0.0127857 1.2 / 2.7 0.9 / 1.4 1.94312 1.89558 0.0209033 5.15086 0.844709 5.99557 16 440 3.0 / 3.4 0.3 / 0.4 0.7 / 1.2 0.6 / 3.9 0.6 / 1.3 12.1208 28.5325 34.0031 36.7384 39.8981 0.0831339 0.0829682 0.0806794 0.246858 84.5983 +20240125_175029__courtyard__results__num_matched5__maxframelookahead10__760p__unified_superglue 38 336 11.0259 0.6 / 24.9 1.4 / 28.5 1.5 / 36.2 47.4342 88.1352 233 144 89 0.2 / 8.0 0.7 / 12.4 32.5 / 35.6 0.15272 0.8 / 16.1 20.1 / 20.2 3.17412 3.0747 0.0622201 11.621 1.8771 13.4981 23 3988 4.0 / 4.5 0.5 / 0.5 47.2 / 45.2 39.5 / 60.2 21.8 / 21.3 0 0 0 0 0 1.79863 1.76169 1.77052 5.33093 198.031 +20240125_175029__delivery_area__results__num_matched10__maxframelookahead0__760p__unified_disk 44 142 14.1697 0.1 / 0.3 0.1 / 1.9 0.1 / 1.9 17.0688 118.846 140 135 5 0.1 / 0.2 0.1 / 1.2 0.2 / 0.3 0.0405178 0.2 / 2.9 19.0 / 23.3 2.71972 2.55285 0.0675545 27.7585 5.57062 33.3291 20 9085 4.0 / 4.4 0.5 / 0.5 0.1 / 0.1 0.1 / 0.4 0.1 / 0.1 39.7338 43.1662 44.3104 44.8825 45.1685 5.40268 5.3806 5.36752 16.1509 238.836 +20240125_175029__delivery_area__results__num_matched10__maxframelookahead0__760p__unified_lightglue 44 142 17.4332 0.1 / 0.2 0.2 / 0.9 0.2 / 0.9 14.0864 42.6506 141 135 6 0.1 / 0.2 0.2 / 0.4 0.1 / 0.1 0.0176089 0.2 / 0.3 19.9 / 27.2 2.76125 2.67392 0.0523295 9.99145 1.71458 11.706 19 2602 4.0 / 4.6 0.5 / 0.6 0.1 / 0.1 0.1 / 0.2 0.1 / 0.1 38.7759 41.4195 42.3006 42.7412 42.9615 1.01756 1.02237 1.01066 3.05068 120.797 +20240125_175029__delivery_area__results__num_matched10__maxframelookahead0__760p__unified_loftr 44 142 15.1689 0.1 / 0.9 0.4 / 2.9 0.4 / 3.0 15.5565 24.6273 137 129 8 0.1 / 0.4 0.4 / 0.8 0.2 / 0.2 0.0233037 0.3 / 0.6 2.2 / 2.3 3.21863 3.05348 0.0201135 14.3107 3.41364 17.7243 20 4055 3.0 / 3.5 0.4 / 0.5 0.1 / 0.1 0.2 / 0.3 0.1 / 0.1 39.9856 43.267 44.3608 44.9077 45.1811 0.954739 0.954078 0.953414 2.86234 109.078 +20240125_175029__delivery_area__results__num_matched10__maxframelookahead0__760p__unified_sift 44 142 14.0999 0.2 / 1.0 0.5 / 4.2 0.5 / 4.3 8.50486 14.313 135 124 11 0.2 / 0.5 0.4 / 1.2 1.0 / 0.9 0.0182333 0.4 / 1.8 4.9 / 7.8 2.89738 2.84338 0.0281684 5.15999 0.567382 5.72737 20 838 4.0 / 4.3 0.3 / 0.3 0.1 / 0.1 0.2 / 0.4 0.2 / 0.2 37.786 42.3871 43.9208 44.6877 45.0711 0.143431 0.146771 0.140959 0.431249 73.2207 +20240125_175029__delivery_area__results__num_matched10__maxframelookahead0__760p__unified_superglue 44 142 15.7315 0.1 / 0.3 0.2 / 0.9 0.2 / 0.9 15.2729 41.5372 138 132 6 0.1 / 0.2 0.2 / 0.5 0.2 / 0.2 0.0160191 0.2 / 0.4 17.7 / 26.6 2.72858 2.65658 0.020005 9.60344 1.67951 11.2829 19 2374 4.0 / 4.5 0.5 / 0.7 0.1 / 0.1 0.2 / 0.4 0.1 / 0.1 37.0293 40.7208 41.9513 42.5666 42.8742 0.960269 0.948982 0.963418 2.87276 118.477 +20240125_175029__delivery_area__results__num_matched10__maxframelookahead10__760p__unified_disk 44 415 11.5644 0.1 / 1.7 0.2 / 5.6 0.2 / 5.8 14.5703 134.467 196 176 20 0.1 / 0.4 0.2 / 2.1 0.3 / 0.3 0.0192802 0.2 / 0.8 18.4 / 26.6 3.35724 3.04873 0.110557 27.9629 5.71048 33.6734 22 10098 4.0 / 4.5 0.5 / 0.5 0.1 / 0.1 0.2 / 0.3 0.1 / 0.1 45.5686 48.2275 49.1137 49.5569 49.7784 3.99334 3.83963 3.91553 11.7486 246.962 +20240125_175029__delivery_area__results__num_matched10__maxframelookahead10__760p__unified_lightglue 44 415 11.4495 0.1 / 0.9 0.3 / 1.8 0.3 / 1.9 11.978 50.5846 202 190 12 0.1 / 0.4 0.3 / 0.9 0.2 / 0.3 0.0203302 0.3 / 0.6 2.2 / 2.1 3.58677 3.49664 0.0490022 10.2063 1.79404 12.0003 30 4540 4.0 / 4.3 0.5 / 0.6 0.0 / 0.1 0.1 / 0.2 0.1 / 0.1 61.6721 65.5779 66.8799 67.5308 67.8563 1.61413 1.55925 1.53659 4.71008 125.117 +20240125_175029__delivery_area__results__num_matched10__maxframelookahead10__760p__unified_loftr 44 415 11.262 0.2 / 1.4 0.6 / 4.4 0.6 / 4.5 12.523 26.8597 168 156 12 0.2 / 0.6 0.5 / 1.4 0.1 / 0.2 0.0257657 0.4 / 1.5 0.6 / 0.8 3.67541 3.5458 0.0600088 14.2942 3.29623 17.5905 25 4428 3.0 / 3.5 0.4 / 0.5 0.2 / 0.2 0.2 / 0.3 0.1 / 0.1 45.5589 52.3144 54.5663 55.6922 56.2552 0.655406 0.661853 0.658563 1.97595 104.417 +20240125_175029__delivery_area__results__num_matched10__maxframelookahead10__760p__unified_sift 44 415 11.5183 0.3 / 1.4 0.5 / 7.6 0.6 / 7.7 8.70803 16.4379 159 145 14 0.2 / 0.6 0.5 / 2.9 0.3 / 0.3 2.75657 0.6 / 4.9 2.2 / 2.1 3.37672 3.29122 0.0603132 4.99009 0.601732 5.59182 22 862 4.0 / 4.3 0.3 / 0.3 0.1 / 0.1 0.3 / 0.4 0.2 / 0.2 39.7458 45.8983 47.9492 48.9746 49.4873 0.164001 0.160002 0.15536 0.479451 75.9082 +20240125_175029__delivery_area__results__num_matched10__maxframelookahead10__760p__unified_superglue 44 415 11.4772 0.1 / 1.7 0.3 / 2.5 0.3 / 2.8 12.0092 49.3996 203 188 15 0.1 / 0.6 0.3 / 1.0 0.2 / 0.2 0.019953 0.3 / 1.7 3.3 / 4.2 3.44576 3.35472 0.0491107 10.6878 1.69552 12.3833 40 5597 4.0 / 4.2 0.5 / 0.6 0.6 / 0.9 0.8 / 7.5 4.5 / 4.9 8.151 20.944 31.5132 52.5546 69.3422 2.54126 2.52007 2.47985 7.54128 128.411 +20240125_175029__delivery_area__results__num_matched5__maxframelookahead0__760p__unified_disk 44 126 11.4064 0.1 / 0.2 0.1 / 1.0 0.1 / 1.0 33.4697 111.29 124 116 8 0.1 / 0.1 0.1 / 0.2 0.1 / 0.1 0.0141854 0.1 / 0.2 16.4 / 26.0 2.54869 2.42895 0.0175231 28.9412 5.75805 34.6993 20 10567 4.0 / 4.4 0.4 / 0.5 0.1 / 0.1 0.1 / 0.3 0.1 / 0.1 41.1476 43.7318 44.5932 45.0239 45.2392 3.8245 3.83348 3.8621 11.5202 242.689 +20240125_175029__delivery_area__results__num_matched5__maxframelookahead0__760p__unified_lightglue 44 126 11.5751 0.1 / 0.1 0.2 / 0.3 0.2 / 0.3 19.9438 39.7862 125 119 6 0.1 / 0.1 0.2 / 0.3 0.1 / 0.1 0.0173845 0.2 / 0.3 17.9 / 26.5 2.66611 2.51951 0.109374 10.2112 1.73016 11.9413 19 2573 4.0 / 4.6 0.5 / 0.6 0.0 / 0.0 0.1 / 0.3 0.1 / 0.1 37.5645 40.9349 42.0584 42.6201 42.901 0.94353 0.943461 0.940909 2.82799 117.395 +20240125_175029__delivery_area__results__num_matched5__maxframelookahead0__760p__unified_loftr 44 126 11.6587 0.1 / 0.9 0.4 / 2.0 0.4 / 2.1 40.6813 24.1191 124 117 7 0.1 / 0.5 0.4 / 0.9 0.2 / 0.2 2.09707 0.3 / 0.6 2.2 / 2.1 2.52103 2.35245 0.0205679 14.6216 3.4593 18.0809 20 3930 3.0 / 3.5 0.4 / 0.5 0.1 / 0.1 0.2 / 0.3 0.2 / 0.2 35.6217 41.5214 43.488 44.4713 44.9629 0.730875 0.714646 0.702811 2.14843 131.304 +20240125_175029__delivery_area__results__num_matched5__maxframelookahead0__760p__unified_sift 44 126 15.8866 0.2 / 0.9 0.5 / 3.3 0.5 / 3.4 13.7481 13.5186 120 108 12 0.2 / 0.4 0.4 / 1.1 1.0 / 0.9 0.0172884 0.4 / 0.7 4.5 / 6.6 2.46261 2.38427 0.0609908 4.98217 0.563419 5.54559 20 824 3.0 / 4.2 0.3 / 0.3 0.1 / 0.1 0.3 / 0.4 0.2 / 0.2 35.211 41.3571 43.4058 44.4302 44.9424 0.137997 0.138438 0.138691 0.415214 78.1035 +20240125_175029__delivery_area__results__num_matched5__maxframelookahead10__760p__unified_disk 44 407 11.506 0.1 / 1.8 0.2 / 5.8 0.3 / 6.0 27.1651 132.477 188 167 21 0.1 / 0.4 0.2 / 2.2 0.3 / 0.3 0.0185564 0.3 / 2.1 16.4 / 17.7 3.14682 2.90981 0.0356245 28.7264 5.63685 34.3632 22 9529 4.0 / 4.3 0.4 / 0.5 0.1 / 0.2 0.2 / 0.9 0.5 / 0.7 25.068 37.2305 43.6686 46.8343 48.4172 3.24244 3.22538 3.26865 9.73658 255.468 +20240125_175029__delivery_area__results__num_matched5__maxframelookahead10__760p__unified_lightglue 44 407 11.406 0.1 / 0.9 0.3 / 1.5 0.3 / 1.6 18.3774 50.0231 194 183 11 0.1 / 0.4 0.3 / 0.9 0.2 / 0.3 0.0202217 0.3 / 0.6 2.5 / 2.4 3.62895 3.54347 0.0284543 10.7268 1.77786 12.5046 30 4510 4.0 / 4.3 0.5 / 0.6 0.1 / 0.1 0.2 / 0.3 0.2 / 0.2 53.8864 62.4637 65.3227 66.7523 67.4671 1.80411 1.78664 1.78574 5.37659 132.341 +20240125_175029__delivery_area__results__num_matched5__maxframelookahead10__760p__unified_loftr 44 407 11.6459 0.2 / 1.6 0.5 / 4.9 0.6 / 5.0 66.2564 26.4692 157 143 14 0.2 / 0.6 0.4 / 1.2 0.1 / 0.1 0.0197814 0.3 / 0.7 1.1 / 1.2 3.05142 2.96193 0.0309777 13.5705 3.30816 16.8786 22 4329 3.0 / 3.5 0.4 / 0.5 0.1 / 0.2 0.3 / 0.4 0.1 / 0.1 40.6513 46.2605 48.1303 49.0651 49.5326 0.723661 0.725851 0.706924 2.15655 156.773 +20240125_175029__delivery_area__results__num_matched5__maxframelookahead10__760p__unified_sift 44 407 11.8566 0.3 / 1.4 0.5 / 7.9 0.6 / 8.0 10.7029 16.3112 151 136 15 0.2 / 0.6 0.5 / 3.0 0.7 / 0.7 0.0189552 0.5 / 1.4 6.1 / 8.8 3.45266 3.38421 0.0443728 4.46781 0.606745 5.07456 40 1796 3.0 / 4.0 0.2 / 0.3 0.2 / 0.2 2.2 / 10.5 5.7 / 8.6 0 0 9.03171 36.5373 58.8247 0.388961 0.391936 0.386469 1.16748 76.6941 +20240125_175029__delivery_area__results__num_matched5__maxframelookahead10__760p__unified_superglue 44 407 11.6159 0.1 / 1.7 0.3 / 2.3 0.3 / 2.6 33.439 48.1646 195 181 14 0.1 / 0.6 0.3 / 1.0 0.2 / 0.2 0.0201554 0.3 / 1.9 1.8 / 1.8 3.45039 3.36744 0.0426874 11.4007 1.74348 13.1442 40 5644 4.0 / 4.2 0.5 / 0.6 0.6 / 0.9 1.1 / 3.7 2.3 / 2.4 9.47212 25.8853 50.6339 70.1641 80.5366 2.10883 2.11715 2.12013 6.34621 148.341 +20240125_175029__electro__results__num_matched10__maxframelookahead0__760p__unified_disk 45 144 13.7196 0.1 / 0.1 0.1 / 0.2 0.1 / 0.2 18.5105 106.323 115 107 8 0.1 / 0.1 0.1 / 0.2 0.0 / 0.0 0.00703239 0.1 / 0.1 0.1 / 0.1 1.34736 1.29155 0.00750303 18.3076 3.37746 21.6851 24 11987 4.0 / 4.5 0.4 / 0.5 0.0 / 0.0 0.1 / 0.1 0.0 / 0.0 51.401 52.5604 52.9469 53.1401 53.2367 4.60527 4.583 4.5359 13.7243 214.605 +20240125_175029__electro__results__num_matched10__maxframelookahead0__760p__unified_lightglue 45 144 17.258 0.1 / 0.3 0.3 / 0.5 0.3 / 0.5 13.1577 24.5242 125 118 7 0.1 / 0.2 0.3 / 0.4 0.1 / 0.1 0.0172818 0.2 / 0.3 0.2 / 0.2 1.79013 1.75273 0.0163441 5.55205 0.782326 6.33438 30 2358 4.0 / 4.4 0.5 / 0.6 0.1 / 0.1 0.2 / 0.2 0.1 / 0.1 61.4852 64.5941 65.6304 66.1485 66.4076 0.976051 0.972196 0.963484 2.91183 94.9856 +20240125_175029__electro__results__num_matched10__maxframelookahead0__760p__unified_loftr 45 144 14.5815 0.1 / 0.8 0.3 / 1.8 0.3 / 1.8 18.1355 25.0359 119 103 16 0.1 / 0.3 0.3 / 0.7 0.1 / 0.2 0.0134659 0.3 / 0.6 2.6 / 2.7 1.49168 1.43421 0.0233076 9.79039 2.07978 11.8702 29 4850 3.0 / 3.5 0.4 / 0.5 0.1 / 0.1 0.8 / 9.1 2.9 / 3.1 0 3.5383 25.5236 44.9366 54.6905 0.877441 0.881641 0.882572 2.64175 104.821 +20240125_175029__electro__results__num_matched10__maxframelookahead0__760p__unified_sift 45 144 14.6971 0.2 / 0.4 0.6 / 0.9 0.6 / 0.9 8.79493 12.7086 90 75 15 0.2 / 0.4 0.6 / 0.9 0.3 / 0.3 0.147352 0.3 / 0.6 4.6 / 7.4 0.724459 0.693686 0.0163319 3.49948 0.177116 3.6766 16 451 3.0 / 3.6 0.2 / 0.3 0.6 / 0.9 0.6 / 8.2 4.9 / 8.0 1.58072 5.097 9.65179 16.197 22.3494 0.177744 0.174383 0.176059 0.528279 68.4179 +20240125_175029__electro__results__num_matched10__maxframelookahead0__760p__unified_superglue 45 144 15.1524 0.1 / 1.7 0.3 / 1.1 0.3 / 2.1 14.1651 23.7072 130 117 13 0.1 / 0.3 0.3 / 0.5 0.2 / 0.2 0.021322 0.3 / 0.3 0.2 / 0.3 1.75874 1.72259 0.0169301 5.40341 0.747982 6.15139 29 2214 4.0 / 4.5 0.5 / 0.6 0.0 / 0.0 0.1 / 0.2 0.0 / 0.1 60.3607 62.8109 63.6277 64.0361 64.2403 0.861931 0.853785 0.849977 2.56578 92.707 +20240125_175029__electro__results__num_matched10__maxframelookahead10__760p__unified_disk 45 465 11.7175 0.1 / 6.4 0.2 / 6.8 0.2 / 8.1 15.0133 129.789 187 149 38 0.1 / 0.2 0.2 / 0.4 0.1 / 0.1 0.0160129 0.2 / 0.3 0.6 / 1.6 3.19956 3.09368 0.0292311 23.3343 4.40081 27.7351 36 14362 4.0 / 4.6 0.5 / 0.6 0.0 / 0.1 0.1 / 1.8 0.3 / 0.7 51.321 65.8617 70.7086 75.4035 77.7017 6.3504 6.37201 6.30259 19.0251 249.578 +20240125_175029__electro__results__num_matched10__maxframelookahead10__760p__unified_lightglue 45 465 11.6083 0.2 / 7.3 0.4 / 9.2 0.4 / 11.4 10.816 31.4136 206 177 29 0.2 / 1.3 0.3 / 2.1 0.4 / 0.4 0.0178592 0.3 / 1.4 19.6 / 20.5 3.72766 3.66369 0.0363235 6.49355 0.976877 7.47043 33 2441 4.0 / 4.4 0.5 / 0.6 0.1 / 0.1 0.2 / 0.3 0.1 / 0.1 66.6093 70.6437 71.9885 72.6609 72.9971 1.67361 1.60419 1.60351 4.88141 99.6327 +20240125_175029__electro__results__num_matched10__maxframelookahead10__760p__unified_loftr 45 465 11.768 0.1 / 1.2 0.3 / 4.0 0.4 / 4.0 13.2603 27.9316 166 150 16 0.1 / 0.5 0.3 / 1.1 0.2 / 0.2 0.0185523 0.3 / 0.4 0.3 / 0.4 3.07585 2.9687 0.0220551 12.7076 2.7317 15.4393 38 7814 3.0 / 3.5 0.3 / 0.5 0.1 / 0.1 0.2 / 0.5 0.1 / 0.2 70.4291 78.8383 81.6414 83.0429 83.7437 1.25738 1.24712 1.2856 3.79021 109.259 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0.0 0.0116391 0.1 / 0.2 0.1 / 0.1 1.30646 1.24633 0.00794697 19.5128 3.58345 23.0963 24 12468 4.0 / 4.5 0.4 / 0.5 0.0 / 0.0 0.1 / 0.1 0.0 / 0.0 51.2357 52.4943 52.9138 53.1236 53.2284 4.88542 4.88669 4.87792 14.6502 220.646 +20240125_175029__electro__results__num_matched5__maxframelookahead0__760p__unified_lightglue 45 119 11.7872 0.1 / 0.2 0.3 / 0.4 0.3 / 0.4 17.3307 23.0929 100 94 6 0.1 / 0.2 0.3 / 0.4 0.1 / 0.1 0.0244877 0.3 / 0.3 0.2 / 0.2 1.67975 1.63603 0.0141931 5.69723 0.788984 6.48621 30 2396 4.0 / 4.4 0.5 / 0.6 0.1 / 0.1 0.2 / 0.2 0.1 / 0.1 61.3504 64.5402 65.6034 66.135 66.4009 0.898464 0.884866 0.878413 2.66184 92.0711 +20240125_175029__electro__results__num_matched5__maxframelookahead0__760p__unified_loftr 45 119 11.7092 0.1 / 1.2 0.2 / 3.3 0.2 / 3.3 38.2941 22.499 97 82 15 0.1 / 0.2 0.2 / 0.5 0.1 / 0.1 0.0108135 0.2 / 0.5 0.9 / 1.1 1.8177 1.7621 0.0205779 9.46829 2.02786 11.4961 30 5029 3.0 / 3.4 0.3 / 0.4 0.1 / 0.1 0.3 / 0.6 0.1 / 0.2 55.994 62.3976 64.5321 65.5994 66.133 0.892172 0.893824 0.884259 2.67036 119.207 +20240125_175029__electro__results__num_matched5__maxframelookahead0__760p__unified_sift 45 119 16.3831 0.1 / 0.3 0.5 / 0.9 0.5 / 0.9 13.8833 11.9705 81 66 15 0.1 / 0.3 0.5 / 0.9 0.3 / 0.3 0.0127752 0.4 / 0.8 5.5 / 7.7 0.726182 0.701406 0.0148351 3.345 0.19256 3.53756 16 434 3.0 / 3.6 0.2 / 0.3 0.8 / 1.1 0.6 / 15.7 5.6 / 7.7 1.11532 5.4671 10.8071 16.3094 23.0508 0.165225 0.169843 0.168851 0.50401 73.562 +20240125_175029__electro__results__num_matched5__maxframelookahead0__760p__unified_superglue 45 119 11.7403 0.1 / 2.0 0.3 / 1.2 0.3 / 2.4 17.2647 22.1523 105 92 13 0.1 / 0.2 0.3 / 0.4 0.2 / 0.2 0.0115607 0.3 / 0.3 0.2 / 0.3 1.5663 1.53404 0.0141087 5.43891 0.773792 6.2127 29 2231 4.0 / 4.5 0.5 / 0.6 0.1 / 0.1 0.1 / 0.2 0.0 / 0.1 60.2278 62.7578 63.6011 64.0228 64.2336 0.695513 0.694067 0.702183 2.09186 90.0247 +20240125_175029__electro__results__num_matched5__maxframelookahead10__760p__unified_disk 45 443 11.9337 0.1 / 7.2 0.2 / 7.6 0.2 / 9.2 30.2301 122.504 165 126 39 0.1 / 0.2 0.2 / 0.4 0.1 / 0.1 0.0150595 0.2 / 0.3 1.6 / 2.4 2.98882 2.8834 0.0336161 25.6435 4.54803 30.1916 36 15039 4.0 / 4.7 0.5 / 0.6 0.0 / 0.0 0.1 / 4.2 2.3 / 3.3 5.13436 19.2957 40.1715 57.906 67.8905 6.80912 6.65724 6.67876 20.1452 261.668 +20240125_175029__electro__results__num_matched5__maxframelookahead10__760p__unified_lightglue 45 443 11.8833 0.2 / 8.1 0.4 / 10.2 0.5 / 12.7 16.8013 30.3818 184 156 28 0.2 / 1.4 0.3 / 2.3 0.3 / 0.4 0.019563 0.3 / 1.5 19.6 / 20.5 3.39797 3.3294 0.0314863 6.67931 1.02447 7.70377 41 2832 4.0 / 4.4 0.5 / 0.6 0.1 / 0.2 0.3 / 26.1 20.9 / 20.5 0 2.33209 5.05518 6.97204 15.1597 0.956871 0.965318 0.936193 2.85851 103.114 +20240125_175029__electro__results__num_matched5__maxframelookahead10__760p__unified_loftr 45 443 11.8285 0.1 / 1.2 0.3 / 3.5 0.3 / 3.6 74.4737 26.0812 147 132 15 0.1 / 0.4 0.3 / 1.1 0.2 / 0.2 0.0161636 0.3 / 0.4 0.2 / 0.5 2.97928 2.90507 0.027416 12.1001 2.67815 14.7783 38 7306 3.0 / 3.5 0.3 / 0.4 0.1 / 0.1 0.2 / 0.5 0.1 / 0.2 70.0496 78.6865 81.5655 83.005 83.7247 1.2175 1.2097 1.19537 3.62268 166.685 +20240125_175029__electro__results__num_matched5__maxframelookahead10__760p__unified_sift 45 443 11.7542 0.2 / 1.0 0.7 / 2.8 0.7 / 2.9 11.0885 15.4115 112 94 18 0.1 / 0.4 0.6 / 1.5 0.5 / 0.6 0.0261998 0.5 / 1.0 11.3 / 12.4 2.41708 2.3347 0.0645959 4.38754 0.35294 4.74049 38 1013 3.0 / 3.8 0.2 / 0.3 3.3 / 4.7 1.6 / 18.2 5.3 / 6.8 0 0 12.8028 31.2465 55.087 1.24976 1.26965 1.27291 3.79242 77.0728 +20240125_175029__electro__results__num_matched5__maxframelookahead10__760p__unified_superglue 45 443 11.8521 0.2 / 4.8 0.4 / 7.1 0.4 / 8.6 24.1668 28.9568 184 157 27 0.2 / 0.5 0.4 / 2.1 0.3 / 0.4 0.0145566 0.4 / 0.6 1.2 / 3.6 3.79182 3.72542 0.0346797 6.52188 0.960835 7.48272 40 2775 4.0 / 4.5 0.5 / 0.6 0.1 / 0.1 0.2 / 1.0 0.2 / 0.4 67.4043 78.9584 82.8125 85.8739 87.3814 1.10326 1.14265 1.08817 3.33418 109.796 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0.5 / 1.4 0.2 / 0.4 0.0471385 0.2 / 0.8 1.6 / 2.3 15.816 15.6605 0.0537143 23.7694 5.1054 28.8748 63 13521 3.0 / 3.9 0.4 / 0.6 0.1 / 0.1 0.2 / 0.3 0.1 / 0.3 62.2621 73.9968 78.4458 80.6702 81.7825 3.45709 3.48489 3.40406 10.3462 261.513 +20240125_175029__facade__results__num_matched10__maxframelookahead0__760p__unified_sift 76 586 21.3504 0.3 / 1.1 1.5 / 6.0 1.5 / 6.0 32.2654 57.7884 405 376 29 0.3 / 0.8 1.3 / 4.6 0.6 / 0.9 0.0514064 1.2 / 2.8 2.0 / 2.9 16.1671 16.0393 0.0706265 13.3988 2.28681 15.6856 63 5023 4.0 / 4.9 0.3 / 0.4 0.1 / 0.2 0.3 / 0.6 0.5 / 0.7 40.6877 63.1596 72.3654 77.7315 80.3131 1.51439 1.49438 1.49161 4.50052 205.151 +20240125_175029__facade__results__num_matched10__maxframelookahead10__760p__unified_disk 76 1023 19.4049 0.1 / 6.9 0.3 / 13.9 0.3 / 16.1 28.4445 606.761 708 629 79 0.1 / 3.3 0.3 / 7.3 0.1 / 0.2 0.0697646 0.2 / 6.1 110.6 / 100.9 27.6954 27.4262 0.158649 29.4466 5.4709 34.9175 64 9259 4.0 / 4.6 0.5 / 0.7 0.1 / 0.1 0.1 / 5.2 3.1 / 8.6 1.82422 7.91456 30.805 51.1999 63.04 4.6758 4.63912 4.63682 13.9519 799.251 +20240125_175029__facade__results__num_matched10__maxframelookahead10__760p__unified_lightglue 76 1023 19.312 0.2 / 18.0 0.5 / 26.7 0.5 / 33.0 28.4319 297.414 850 642 208 0.1 / 6.4 0.3 / 11.7 24.8 / 42.4 0.0859635 0.2 / 14.5 62.8 / 76.1 27.8511 27.5117 0.264946 19.5006 3.09632 22.5969 70 6990 3.0 / 4.5 0.6 / 0.7 6.2 / 17.7 0.2 / 27.3 78.9 / 75.8 0 0 0 0.889497 2.53731 8.85661 8.84744 8.84071 26.5449 484.688 +20240125_175029__facade__results__num_matched10__maxframelookahead10__760p__unified_loftr 76 1023 19.5113 0.2 / 0.7 0.5 / 2.7 0.5 / 2.8 28.9006 58.8939 496 478 18 0.1 / 0.4 0.5 / 1.6 0.2 / 0.5 0.0792127 0.3 / 0.8 1.0 / 1.6 24.3155 24.0627 0.124188 25.229 5.44352 30.6725 62 13229 3.0 / 4.0 0.4 / 0.6 0.1 / 0.1 0.1 / 0.2 0.1 / 0.3 63.0469 72.5872 77.2388 79.4089 80.4939 3.42883 3.40897 3.42519 10.2631 237.913 +20240125_175029__facade__results__num_matched10__maxframelookahead10__760p__unified_sift 76 1023 19.6352 0.4 / 1.4 1.6 / 6.3 1.6 / 6.4 20.5417 70.1827 493 461 32 0.3 / 1.1 1.4 / 4.9 1.0 / 1.5 0.0662959 1.0 / 3.4 4.4 / 17.5 22.9774 22.8101 0.104693 14.2982 2.27938 16.5776 65 4977 4.0 / 5.1 0.3 / 0.4 0.2 / 0.4 0.3 / 1.1 0.7 / 1.4 27.4558 53.4068 66.1011 74.4899 79.8469 1.80831 1.78412 1.78571 5.37824 213.118 +20240125_175029__facade__results__num_matched10__maxframelookahead10__760p__unified_superglue 76 1023 19.6952 0.2 / 16.0 0.5 / 24.0 0.5 / 29.7 28.6337 313.978 812 619 193 0.1 / 6.2 0.3 / 10.5 3.7 / 9.8 0.41984 0.3 / 12.6 105.4 / 96.4 28.5446 28.2947 0.161538 20.7842 3.23669 24.0209 72 6249 3.0 / 4.5 0.6 / 0.7 5.1 / 11.0 0.2 / 42.4 105.7 / 95.3 0 0 0 1.06449 2.95443 19.555 19.4218 19.4998 58.4766 536.285 +20240125_175029__facade__results__num_matched5__maxframelookahead0__760p__unified_disk 76 328 19.2775 0.1 / 7.7 0.4 / 14.5 0.4 / 17.2 71.7675 340.161 281 239 42 0.1 / 5.5 0.3 / 8.8 0.5 / 0.9 0.046149 0.3 / 11.2 24.3 / 42.4 11.8092 11.553 0.122879 45.6432 8.73035 54.3736 58 16817 4.0 / 4.8 0.5 / 0.6 0.1 / 0.1 0.1 / 5.0 1.4 / 6.5 22.8082 34.1189 45.1364 56.4036 63.7509 10.2718 10.3409 10.1808 30.7936 604.003 +20240125_175029__facade__results__num_matched5__maxframelookahead0__760p__unified_lightglue 76 328 19.0812 0.1 / 10.1 0.3 / 16.9 0.3 / 20.7 49.9706 166.92 288 244 44 0.1 / 8.2 0.3 / 8.6 15.4 / 28.2 0.0388207 0.3 / 8.8 81.5 / 75.3 11.5283 11.1758 0.160288 33.6707 5.60541 39.2761 69 11677 4.0 / 5.1 0.6 / 0.7 15.7 / 28.5 0.1 / 24.8 26.9 / 43.0 0 0 0 0 4.89622 6.20987 6.1073 6.10907 18.4264 373.205 +20240125_175029__facade__results__num_matched5__maxframelookahead0__760p__unified_loftr 76 328 19.3233 0.1 / 0.3 0.4 / 1.8 0.4 / 1.9 94.7753 45.4974 241 222 19 0.1 / 0.2 0.4 / 1.4 0.1 / 0.2 0.0300419 0.3 / 0.6 0.4 / 0.7 12.2969 12.146 0.0473623 20.8911 4.71236 25.6035 61 12268 3.0 / 3.7 0.4 / 0.5 0.2 / 0.3 0.3 / 0.5 0.2 / 0.4 51.7326 67.823 74.0431 77.1531 78.7081 2.59968 2.51819 2.50085 7.61883 267.259 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80.826 4.39478 4.2948 4.30494 12.9946 322.574 +20240125_175029__facade__results__num_matched5__maxframelookahead10__760p__unified_sift 76 833 19.4349 0.3 / 1.3 1.4 / 6.1 1.4 / 6.1 52.8951 61.0357 381 360 21 0.3 / 1.1 1.3 / 4.2 1.1 / 1.7 0.0533705 0.8 / 3.2 4.6 / 12.4 19.4459 19.2196 0.0682516 14.0916 2.57202 16.6636 64 5370 4.0 / 4.9 0.3 / 0.4 0.1 / 0.2 0.3 / 0.8 0.4 / 0.6 46.3624 65.1182 73.9737 79.0921 81.6513 1.6252 1.58424 1.54973 4.75927 231.785 +20240125_175029__facade__results__num_matched5__maxframelookahead10__760p__unified_superglue 76 833 19.0093 0.2 / 18.3 0.5 / 25.4 0.5 / 32.1 112.281 258.944 643 469 174 0.1 / 7.3 0.3 / 9.8 6.2 / 16.7 4.81689 0.3 / 10.5 68.2 / 72.7 23.058 22.8683 0.107622 23.5903 3.8969 27.4872 73 7216 4.0 / 4.7 0.6 / 0.7 7.0 / 19.4 0.7 / 40.9 66.5 / 74.6 0 0 0 1.16164 2.99067 15.4752 15.4328 15.4648 46.3728 555.421 +20240125_175029__kicker__results__num_matched10__maxframelookahead0__760p__unified_disk 31 99 9.99767 0.2 / 1.0 0.3 / 1.0 0.3 / 1.5 13.7334 59.4296 94 84 10 0.2 / 0.4 0.3 / 0.9 0.1 / 0.1 0.00896239 0.3 / 0.7 0.2 / 0.4 0.969903 0.936871 0.00831938 9.84877 1.82343 11.6722 19 5169 3.0 / 4.2 0.5 / 0.6 0.0 / 0.0 0.1 / 0.1 0.1 / 0.1 55.9916 59.1708 60.2306 60.7605 61.0254 2.043 2.03392 2.00049 6.07751 128.763 +20240125_175029__kicker__results__num_matched10__maxframelookahead0__760p__unified_lightglue 31 99 9.70079 0.2 / 0.4 0.4 / 2.3 0.4 / 2.3 11.148 22.9977 94 88 6 0.2 / 0.4 0.4 / 2.3 0.1 / 0.1 0.00635076 0.3 / 0.7 0.2 / 0.3 1.13874 1.0993 0.0094614 5.11495 0.597356 5.71231 19 1557 4.0 / 4.6 0.5 / 0.6 0.1 / 0.1 0.1 / 0.2 0.1 / 0.2 51.8238 57.5037 59.397 60.3437 60.817 0.59762 0.593575 0.586456 1.77775 76.1768 +20240125_175029__kicker__results__num_matched10__maxframelookahead0__760p__unified_loftr 31 99 9.81843 0.3 / 1.5 0.5 / 4.7 0.6 / 5.1 11.6532 8.77319 80 72 8 0.2 / 0.7 0.4 / 4.5 0.1 / 0.1 0.00648403 0.4 / 0.9 0.3 / 0.5 1.18263 1.15934 0.00975823 4.2597 0.623058 4.88276 19 1123 3.0 / 3.5 0.4 / 0.5 0.1 / 0.1 0.2 / 0.3 0.2 / 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5.20562 22 1290 3.0 / 3.5 0.4 / 0.5 0.1 / 0.1 0.2 / 0.4 0.2 / 0.2 56.8267 65.3113 68.1395 69.5536 70.2607 0.16607 0.173373 0.16749 0.507019 56.4953 +20240125_175029__kicker__results__num_matched10__maxframelookahead10__760p__unified_sift 31 303 9.64381 0.8 / 3.2 1.1 / 6.6 1.3 / 6.8 6.93481 13.5177 134 115 19 0.6 / 1.2 0.9 / 2.1 0.3 / 0.7 0.00919056 0.7 / 1.9 1.1 / 1.6 1.9938 1.95263 0.0248098 4.57433 0.637374 5.2117 27 1475 4.0 / 4.4 0.3 / 0.4 0.2 / 0.3 0.2 / 0.7 0.3 / 0.6 54.7728 70.2962 78.5519 82.8243 84.9606 0.448557 0.451642 0.452831 1.35312 62.2799 +20240125_175029__kicker__results__num_matched10__maxframelookahead10__760p__unified_superglue 31 303 9.71012 0.4 / 18.9 0.5 / 12.8 0.7 / 20.1 8.64796 31.3254 191 161 30 0.3 / 5.1 0.4 / 3.6 0.2 / 0.2 0.0135419 0.4 / 1.0 1.5 / 2.3 2.66084 2.60007 0.0325439 4.92936 0.671857 5.60122 30 1779 3.0 / 4.2 0.5 / 0.6 0.1 / 0.1 0.2 / 0.6 0.2 / 0.3 70.6333 85.7325 91.2533 94.0138 95.394 0.622593 0.616161 0.619837 1.85868 83.8556 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0.1 / 0.1 0.00556111 0.3 / 0.5 0.3 / 0.3 0.946295 0.921199 0.0110409 4.17201 0.552903 4.72492 18 1029 3.0 / 3.4 0.4 / 0.5 0.1 / 0.1 0.3 / 0.4 0.2 / 0.2 44.7217 52.7274 55.3959 56.7302 57.3974 0.132364 0.126765 0.128409 0.387643 71.9523 +20240125_175029__kicker__results__num_matched5__maxframelookahead0__760p__unified_sift 31 79 13.8918 0.3 / 1.1 0.8 / 2.9 0.9 / 2.9 10.9558 10.3254 76 68 8 0.2 / 0.6 0.7 / 1.9 0.2 / 0.3 0.00513959 0.7 / 2.6 0.5 / 0.7 1.08667 1.04817 0.0224319 3.90687 0.471285 4.37815 18 1308 3.0 / 4.1 0.3 / 0.3 0.0 / 0.0 0.1 / 0.2 0.1 / 0.1 53.5487 56.2582 57.1613 57.6129 57.8387 0.547565 0.533237 0.541877 1.62278 65.2119 +20240125_175029__kicker__results__num_matched5__maxframelookahead0__760p__unified_superglue 31 79 9.83949 0.1 / 0.3 0.3 / 1.1 0.3 / 1.1 16.2843 20.4636 74 67 7 0.1 / 0.3 0.3 / 1.2 0.1 / 0.1 0.00468397 0.4 / 1.0 0.3 / 0.4 0.878457 0.851493 0.00709176 4.33524 0.589489 4.92473 18 1745 3.0 / 4.4 0.5 / 0.6 0.1 / 0.1 0.1 / 0.2 0.1 / 0.1 50.5881 55.074 56.5692 57.3169 57.6907 0.573319 0.568712 0.584984 1.7271 77.7009 +20240125_175029__kicker__results__num_matched5__maxframelookahead10__760p__unified_disk 31 290 9.66177 0.3 / 3.9 0.4 / 3.1 0.5 / 4.5 23.6135 74.6641 158 140 18 0.3 / 0.8 0.3 / 1.1 0.4 / 0.5 0.0115697 0.4 / 0.7 0.7 / 1.1 2.27753 2.22661 0.0178039 11.5697 2.06164 13.6313 29 5451 3.0 / 4.3 0.5 / 0.6 0.0 / 0.1 0.1 / 0.3 0.1 / 0.2 78.4298 87.501 90.5247 92.0365 92.7925 2.19648 2.18002 2.16803 6.54464 158.397 +20240125_175029__kicker__results__num_matched5__maxframelookahead10__760p__unified_lightglue 31 290 9.71299 0.4 / 12.9 0.5 / 10.3 0.7 / 15.1 14.3918 29.1813 171 149 22 0.4 / 0.7 0.4 / 2.0 0.1 / 0.2 0.0142422 0.3 / 0.7 0.4 / 0.5 2.37287 2.30178 0.0521843 5.1157 0.737795 5.8535 30 1969 3.0 / 4.4 0.5 / 0.6 0.1 / 0.1 0.1 / 0.2 0.1 / 0.1 83.693 91.5417 94.1579 95.4661 96.1201 0.76724 0.767751 0.754792 2.28988 88.3849 +20240125_175029__kicker__results__num_matched5__maxframelookahead10__760p__unified_loftr 31 290 9.91382 0.3 / 2.0 0.5 / 7.4 0.7 / 7.4 48.3455 9.50465 84 69 15 0.2 / 0.5 0.4 / 1.1 0.1 / 0.1 0.0107687 0.4 / 0.5 0.3 / 0.3 1.30443 1.27001 0.0189083 4.37044 0.598712 4.96915 22 1135 3.0 / 3.4 0.3 / 0.5 0.1 / 0.2 0.3 / 0.5 0.2 / 0.2 52.8384 63.716 67.3419 69.1548 70.0613 0.154007 0.156178 0.151386 0.461658 96.8097 +20240125_175029__kicker__results__num_matched5__maxframelookahead10__760p__unified_sift 31 290 10.1254 0.7 / 3.3 1.1 / 7.0 1.3 / 7.2 8.60294 13.1259 122 103 19 0.5 / 1.1 0.9 / 2.1 0.4 / 0.7 0.00961709 0.7 / 1.9 1.1 / 1.5 1.86204 1.82215 0.0225735 4.07479 0.530015 4.6048 26 1515 4.0 / 4.3 0.3 / 0.4 0.1 / 0.2 0.2 / 0.6 0.3 / 0.5 50.4324 68.5601 76.4489 80.1599 82.0154 0.468664 0.470644 0.473624 1.41302 63.3242 +20240125_175029__kicker__results__num_matched5__maxframelookahead10__760p__unified_superglue 31 290 9.74747 0.5 / 20.1 0.6 / 13.6 0.8 / 21.4 21.0243 29.2756 179 146 33 0.3 / 5.6 0.4 / 3.9 5.7 / 11.0 0.0155866 0.4 / 2.0 7.3 / 13.6 2.64537 2.58327 0.0396626 4.96722 0.673309 5.64053 30 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/ 4.0 0.3 / 0.4 0.0 / 0.0 0.3 / 0.4 0.1 / 0.1 39.2263 41.0751 41.6914 41.9996 42.1536 0.0403006 0.0397263 0.0381162 0.118214 40.3802 +20240125_175029__office__results__num_matched10__maxframelookahead0__760p__unified_superglue 26 63 9.77577 0.2 / 0.3 0.5 / 1.5 0.5 / 1.5 8.90551 11.5025 52 45 7 0.1 / 0.3 0.4 / 1.4 0.1 / 0.1 0.0033443 0.4 / 0.8 0.1 / 0.2 0.804741 0.746164 0.0324259 4.05252 0.312451 4.36497 13 649 4.0 / 4.8 0.6 / 0.7 0.0 / 0.0 0.1 / 0.2 0.0 / 0.1 47.5344 49.0138 49.5069 49.7534 49.8767 0.271948 0.269977 0.270281 0.812303 55.5856 +20240125_175029__office__results__num_matched10__maxframelookahead10__760p__unified_disk 26 216 8.12863 0.3 / 4.3 0.7 / 11.2 0.8 / 11.8 8.05221 37.1237 85 58 27 0.2 / 0.4 0.4 / 1.1 0.2 / 0.2 0.00477123 0.4 / 0.6 0.2 / 0.3 0.913194 0.88808 0.00672841 6.37993 1.03621 7.41614 16 3013 4.0 / 4.2 0.5 / 0.7 0.1 / 0.1 0.2 / 0.5 0.1 / 0.1 53.9564 58.5056 60.0221 60.7803 61.1594 1.18138 1.17735 1.1573 3.51612 85.4002 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4.23973 14 755 3.0 / 3.2 0.3 / 0.4 0.1 / 0.2 0.5 / 12.3 4.2 / 10.4 9.04851 20.0921 35.102 54.186 66.3787 0.116475 0.11436 0.110626 0.341542 29.6477 +20240125_175029__pipes__results__num_matched10__maxframelookahead10__760p__unified_sift 14 86 6.09912 0.4 / 1.2 1.2 / 4.5 1.2 / 4.6 2.68198 3.35466 20 12 8 0.3 / 0.6 1.1 / 0.9 0.2 / 0.2 0.00164223 0.8 / 1.0 0.5 / 0.9 0.425845 0.419587 0.00194025 1.7055 0.0625968 1.76809 6 118 3.0 / 3.6 0.2 / 0.3 0.1 / 0.1 0.3 / 0.5 0.4 / 0.4 28.7958 37.2326 40.0449 41.451 42.1541 0.0132711 0.0138476 0.0130408 0.0402284 25.2535 +20240125_175029__pipes__results__num_matched10__maxframelookahead10__760p__unified_superglue 14 86 6.33296 0.3 / 5.1 0.6 / 6.7 0.8 / 7.9 3.79231 11.0814 59 46 13 0.3 / 0.7 0.4 / 1.6 0.2 / 0.2 0.00345683 0.4 / 0.8 0.5 / 0.8 0.774304 0.755657 0.00665021 3.73047 0.491568 4.22204 13 1280 3.0 / 3.9 0.5 / 0.7 0.2 / 0.3 0.2 / 0.8 0.7 / 1.1 37.8492 62.3755 74.7182 84.3752 88.6162 0.483146 0.489988 0.478217 1.45145 39.8032 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3.5 0.1 / 0.1 0.00300765 0.2 / 0.3 0.1 / 0.1 0.560837 0.548029 0.00341034 3.62571 0.419578 4.04528 12 1080 4.0 / 4.3 0.2 / 0.3 0.0 / 0.0 0.0 / 0.1 0.0 / 0.0 37.6499 38.2858 38.4977 38.6037 38.6567 0.161173 0.201639 0.165453 0.528356 54.0527 +20240125_175029__relief_2__results__num_matched5__maxframelookahead10__760p__unified_superglue 31 261 9.19568 0.1 / 0.3 0.2 / 1.0 0.2 / 1.0 18.5606 33.8199 134 133 1 0.1 / 0.2 0.2 / 0.7 0.1 / 0.1 0.0133882 0.2 / 0.3 0.2 / 0.2 1.69013 1.65435 0.0132184 8.1803 1.33326 9.51355 31 3564 4.0 / 4.9 0.5 / 0.6 0.1 / 0.1 0.1 / 0.2 0.1 / 0.1 88.0371 95.2149 97.6074 98.8037 99.4019 1.22397 1.21121 1.19806 3.63335 101.072 +20240125_175029__relief__results__num_matched10__maxframelookahead0__760p__unified_disk 31 141 9.20294 0.1 / 0.8 0.1 / 1.6 0.1 / 1.8 13.6758 148.676 128 125 3 0.1 / 0.2 0.1 / 0.4 0.2 / 0.3 0.0114758 0.3 / 0.5 0.6 / 1.2 0.94574 0.889039 0.0127418 13.0505 2.73596 15.7865 18 9466 4.0 / 3.9 0.4 / 0.5 0.3 / 0.4 0.2 / 0.4 0.1 / 0.3 33.3687 47.7351 52.8998 55.4822 56.7733 2.75228 2.79641 2.68685 8.23567 225.712 +20240125_175029__relief__results__num_matched10__maxframelookahead0__760p__unified_lightglue 31 141 9.03954 0.1 / 0.2 0.1 / 0.3 0.2 / 0.4 11.1935 41.9429 122 122 0 0.1 / 0.2 0.1 / 0.3 0.3 / 0.2 0.00848126 0.2 / 0.4 0.8 / 1.3 0.914939 0.891084 0.00870991 4.7354 0.67028 5.40568 18 2004 4.0 / 4.1 0.5 / 0.6 0.1 / 0.1 0.2 / 0.2 0.2 / 0.2 48.7173 54.3256 56.1951 57.1298 57.5972 0.618559 0.623452 0.620986 1.86309 92.83 +20240125_175029__relief__results__num_matched10__maxframelookahead0__760p__unified_loftr 31 141 9.13334 0.1 / 0.4 0.2 / 1.5 0.2 / 1.6 18.8157 30.7835 116 113 3 0.1 / 0.3 0.2 / 0.3 0.3 / 0.3 0.00842333 0.2 / 0.4 0.3 / 1.1 0.847038 0.804919 0.00963831 7.31204 1.47757 8.78961 12 3616 3.0 / 3.3 0.3 / 0.4 0.4 / 0.4 0.4 / 0.5 0.3 / 0.5 18.3888 29.8683 34.289 36.4993 37.6045 0.521467 0.529601 0.533665 1.58485 93.2465 +20240125_175029__relief__results__num_matched10__maxframelookahead0__760p__unified_sift 31 141 9.14233 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/ 4.0 0.4 / 0.5 0.1 / 0.1 0.1 / 0.1 0.0 / 0.1 53.1788 56.1102 57.0874 57.5759 57.8202 3.07288 2.89279 2.89482 8.86059 231.839 +20240125_175029__relief__results__num_matched10__maxframelookahead10__760p__unified_lightglue 31 271 9.52745 0.1 / 0.3 0.2 / 0.5 0.2 / 0.6 8.93058 44.3965 144 144 0 0.1 / 0.3 0.2 / 0.5 0.1 / 0.2 0.00772333 0.3 / 0.4 0.6 / 1.0 0.866474 0.841534 0.00743222 4.74564 0.689806 5.43544 18 2032 4.0 / 4.2 0.5 / 0.6 0.1 / 0.1 0.1 / 0.2 0.1 / 0.1 51.7777 55.5498 56.8072 57.4358 57.7502 0.66672 0.661974 0.650899 1.97968 93.6822 +20240125_175029__relief__results__num_matched10__maxframelookahead10__760p__unified_loftr 31 271 9.23152 0.1 / 0.9 0.2 / 2.8 0.2 / 3.0 15.2498 32.5986 124 116 8 0.1 / 0.2 0.2 / 0.3 0.2 / 0.2 0.00857687 0.2 / 0.3 0.4 / 0.9 0.756622 0.711236 0.0142429 6.97163 1.4904 8.46203 18 4127 3.0 / 3.4 0.3 / 0.4 0.4 / 0.3 0.4 / 0.6 0.1 / 0.2 37.7329 49.9319 53.9982 56.0314 57.0479 0.566739 0.552184 0.554539 1.67356 91.9341 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0.4 0.4 / 0.3 0.4 / 0.6 0.1 / 0.2 37.7329 49.9319 53.9982 56.0314 57.0479 0.55919 0.560659 0.557744 1.6777 122.9 +20240125_175029__relief__results__num_matched5__maxframelookahead10__760p__unified_sift 31 263 9.18936 0.3 / 2.4 0.7 / 3.5 0.8 / 3.6 7.49732 12.6295 117 96 21 0.2 / 0.5 0.6 / 1.1 0.2 / 0.2 0.00700164 0.4 / 0.5 0.2 / 0.3 0.970715 0.949582 0.00829172 4.79764 0.602715 5.40036 18 1864 4.0 / 4.7 0.2 / 0.3 0.0 / 0.0 0.1 / 0.1 0.1 / 0.1 54.1885 56.5141 57.2893 57.6769 57.8707 0.530611 0.527327 0.519015 1.57704 59.3324 +20240125_175029__relief__results__num_matched5__maxframelookahead10__760p__unified_superglue 31 263 9.22828 0.1 / 0.6 0.2 / 1.0 0.2 / 1.1 22.5141 44.1386 144 140 4 0.1 / 0.4 0.2 / 0.6 0.2 / 0.2 0.0068152 0.6 / 1.3 10.2 / 14.3 1.00521 0.971521 0.0177543 4.66078 0.678033 5.33882 18 1788 4.0 / 4.2 0.5 / 0.6 3.1 / 4.1 0.4 / 12.0 10.6 / 14.7 0 0 6.24665 15.6316 25.6065 4.24572 4.24271 4.23956 12.7281 117.378 +20240125_175029__terrace__results__num_matched10__maxframelookahead0__760p__unified_disk 23 77 7.85746 0.1 / 0.1 0.2 / 0.3 0.2 / 0.3 10.182 62.5975 72 69 3 0.1 / 0.1 0.2 / 0.3 0.0 / 0.1 0.0065043 0.2 / 0.3 0.1 / 0.1 0.750216 0.70534 0.0151787 10.0812 1.80362 11.8848 13 6378 4.0 / 4.6 0.5 / 0.6 0.0 / 0.0 0.1 / 0.2 0.0 / 0.1 53.4638 55.2986 55.9102 56.2159 56.3688 2.29703 2.28029 2.29342 6.87087 122.758 +20240125_175029__terrace__results__num_matched10__maxframelookahead0__760p__unified_lightglue 23 77 7.64968 0.1 / 0.5 0.3 / 2.0 0.3 / 2.0 8.41043 19.4814 74 69 5 0.1 / 0.3 0.3 / 1.0 0.1 / 0.1 0.00361514 0.2 / 0.3 0.1 / 0.1 0.952512 0.932518 0.00871539 3.98711 0.492386 4.47949 13 1518 4.0 / 4.6 0.5 / 0.6 0.0 / 0.0 0.2 / 0.2 0.1 / 0.1 53.1903 55.1892 55.8554 56.1886 56.3552 0.663765 0.668379 0.6758 2.00803 60.6046 +20240125_175029__terrace__results__num_matched10__maxframelookahead0__760p__unified_loftr 23 77 7.61763 0.1 / 1.2 0.3 / 4.9 0.3 / 4.9 8.34698 10.5511 74 64 10 0.1 / 0.2 0.3 / 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55.3056 55.9137 56.2177 56.3697 0.605076 0.603696 0.607029 1.81595 61.9754 +20240125_175029__terrace__results__num_matched10__maxframelookahead10__760p__unified_disk 23 178 7.82429 0.1 / 1.4 0.3 / 4.3 0.3 / 4.5 8.01694 76.0211 118 111 7 0.1 / 0.5 0.3 / 2.0 0.1 / 0.1 0.0134768 0.5 / 3.5 10.0 / 9.3 1.31617 1.25363 0.0132785 17.3577 3.54214 20.8999 23 11206 3.0 / 4.0 0.5 / 0.6 0.2 / 0.3 0.4 / 1.6 0.5 / 0.6 45.1837 77.8649 88.9325 94.4662 97.2331 4.23538 4.233 4.20673 12.6752 153.47 +20240125_175029__terrace__results__num_matched10__maxframelookahead10__760p__unified_lightglue 23 178 7.68192 0.2 / 3.0 0.4 / 5.5 0.4 / 6.0 6.26766 24.5874 121 112 9 0.2 / 0.6 0.3 / 1.7 0.1 / 0.1 0.0102017 0.3 / 0.4 0.5 / 0.5 1.36559 1.33606 0.0106952 5.85574 0.951127 6.80687 23 2965 4.0 / 4.6 0.5 / 0.6 0.0 / 0.1 0.2 / 0.3 0.1 / 0.1 91.601 96.6404 98.3202 99.1601 99.58 1.04246 1.02385 1.02083 3.08725 69.3473 +20240125_175029__terrace__results__num_matched10__maxframelookahead10__760p__unified_loftr 23 178 7.76851 0.2 / 1.6 0.3 / 3.7 0.3 / 3.9 5.81417 11.6904 98 90 8 0.2 / 0.4 0.3 / 0.8 0.1 / 0.1 0.00824118 0.2 / 0.4 0.2 / 0.2 1.10494 1.072 0.0088973 8.36939 1.58806 9.95746 23 3643 3.0 / 3.4 0.4 / 0.5 0.1 / 0.1 0.2 / 0.2 0.1 / 0.1 89.7767 95.9107 97.9554 98.9777 99.4888 0.503283 0.520451 0.506865 1.5307 56.8246 +20240125_175029__terrace__results__num_matched10__maxframelookahead10__760p__unified_sift 23 178 7.87555 0.3 / 0.7 0.9 / 2.2 0.9 / 2.2 4.815 8.36631 70 65 5 0.3 / 0.6 0.9 / 2.0 0.2 / 0.2 0.0052278 0.9 / 1.5 0.8 / 0.8 0.726856 0.708667 0.0100474 3.87057 0.202995 4.07356 15 491 3.0 / 3.9 0.3 / 0.4 0.1 / 0.1 0.3 / 0.3 0.1 / 0.2 55.5618 61.3552 63.2863 64.2518 64.7346 0.074847 0.0739491 0.0760829 0.224959 42.7477 +20240125_175029__terrace__results__num_matched10__maxframelookahead10__760p__unified_superglue 23 178 7.71641 0.2 / 2.1 0.4 / 2.5 0.4 / 3.6 6.29073 25.4469 120 113 7 0.2 / 0.5 0.3 / 1.7 0.2 / 0.2 0.0100043 0.3 / 0.5 0.4 / 0.5 1.35221 1.31688 0.0154762 6.22214 0.9754 7.19754 23 3062 4.0 / 4.7 0.5 / 0.6 0.1 / 0.1 0.2 / 0.2 0.1 / 0.1 92.0163 96.8065 98.4033 99.2016 99.6008 1.02458 1.01389 1.02825 3.06683 70.5201 +20240125_175029__terrace__results__num_matched5__maxframelookahead0__760p__unified_disk 23 65 8.05603 0.1 / 0.1 0.3 / 0.3 0.3 / 0.3 19.7865 58.3109 61 58 3 0.1 / 0.1 0.3 / 0.3 0.0 / 0.1 0.00609398 0.2 / 0.3 0.1 / 0.1 0.859251 0.820899 0.00777459 10.7896 2.00826 12.7979 13 6703 4.0 / 4.6 0.5 / 0.6 0.0 / 0.0 0.1 / 0.2 0.1 / 0.1 53.4529 55.2942 55.908 56.2149 56.3683 2.51394 2.44992 2.44509 7.40908 130.003 +20240125_175029__terrace__results__num_matched5__maxframelookahead0__760p__unified_lightglue 23 65 7.85005 0.1 / 0.4 0.3 / 1.5 0.3 / 1.5 11.5658 18.1678 62 58 4 0.1 / 0.2 0.3 / 0.9 0.1 / 0.1 0.00355577 0.3 / 0.4 0.2 / 0.2 0.811505 0.790456 0.0078547 4.23262 0.577485 4.8101 13 1633 4.0 / 4.7 0.5 / 0.6 0.0 / 0.0 0.1 / 0.1 0.1 / 0.1 53.2209 55.2014 55.8616 56.1917 56.3567 0.489826 0.489774 0.497145 1.47684 62.5001 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0.3 / 0.6 0.1 / 0.1 0.00376749 0.3 / 0.4 0.2 / 0.2 0.673733 0.656149 0.00571132 4.49965 0.5831 5.08275 13 1709 4.0 / 4.8 0.5 / 0.6 0.0 / 0.0 0.1 / 0.1 0.0 / 0.1 53.6227 55.3621 55.9419 56.2318 56.3768 0.584039 0.595525 0.586122 1.76578 65.0691 +20240125_175029__terrace__results__num_matched5__maxframelookahead10__760p__unified_disk 23 176 7.59911 0.1 / 1.5 0.3 / 4.3 0.3 / 4.5 15.9512 75.4047 117 110 7 0.1 / 0.5 0.3 / 2.0 0.1 / 0.1 0.0133579 0.8 / 3.9 9.8 / 9.3 1.3984 1.33593 0.0136256 17.2959 3.38538 20.6813 23 10904 3.0 / 4.0 0.5 / 0.6 0.2 / 0.2 0.4 / 1.5 0.5 / 0.6 44.7316 77.8926 88.9463 94.4732 97.2366 4.64369 4.59453 4.57211 13.8104 160.965 +20240125_175029__terrace__results__num_matched5__maxframelookahead10__760p__unified_lightglue 23 176 7.6766 0.2 / 3.0 0.4 / 5.6 0.4 / 6.1 10.4238 24.6922 119 110 9 0.2 / 0.6 0.3 / 1.8 0.1 / 0.1 0.0115566 0.3 / 0.4 0.5 / 0.5 1.24382 1.21321 0.0115299 5.97017 0.959653 6.92982 23 2971 4.0 / 4.6 0.5 / 0.6 0.0 / 0.1 0.2 / 0.3 0.1 / 0.1 91.3881 96.5552 98.2776 99.1388 99.5694 1.04465 1.05194 1.05347 3.15016 73.5441 +20240125_175029__terrace__results__num_matched5__maxframelookahead10__760p__unified_loftr 23 176 7.73958 0.2 / 1.6 0.3 / 3.6 0.3 / 3.8 28.9239 11.2363 96 88 8 0.2 / 0.4 0.3 / 0.6 0.1 / 0.1 0.00918961 0.2 / 0.4 0.2 / 0.2 1.13412 1.0988 0.00910163 7.90019 1.57045 9.47064 23 3600 3.0 / 3.4 0.4 / 0.5 0.1 / 0.1 0.2 / 0.2 0.1 / 0.1 89.8536 95.9414 97.9707 98.9854 99.4927 0.508248 0.500805 0.504222 1.51337 78.8781 +20240125_175029__terrace__results__num_matched5__maxframelookahead10__760p__unified_sift 23 176 7.66742 0.3 / 0.7 0.9 / 2.2 0.9 / 2.2 6.13191 8.06871 68 63 5 0.3 / 0.6 0.9 / 2.0 0.2 / 0.2 0.00536609 1.1 / 1.6 0.6 / 0.8 0.735774 0.714314 0.0138886 2.98177 0.19175 3.17352 15 484 3.0 / 3.9 0.3 / 0.4 0.1 / 0.1 0.3 / 0.3 0.1 / 0.1 56.5474 61.7494 63.4834 64.3504 64.7839 0.0710895 0.0708301 0.0721688 0.214167 42.7353 +20240125_175029__terrace__results__num_matched5__maxframelookahead10__760p__unified_superglue 23 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7.0506 18 3172 4.0 / 4.7 0.5 / 0.6 0.0 / 0.0 0.1 / 0.1 0.0 / 0.1 40.1374 41.7692 42.3132 42.5852 42.7212 0.980077 0.968166 0.962521 2.91086 113.588 +20240125_175029__terrains__results__num_matched10__maxframelookahead0__760p__unified_loftr 42 163 11.2076 0.2 / 1.2 0.4 / 3.0 0.5 / 3.2 18.2723 26.1347 139 133 6 0.2 / 0.6 0.4 / 0.9 0.1 / 0.1 0.00521302 0.2 / 0.2 0.1 / 0.2 1.02003 0.975217 0.00511718 11.2496 2.39933 13.649 18 7841 3.0 / 3.7 0.5 / 0.6 0.1 / 0.1 0.1 / 0.2 0.0 / 0.1 36.4652 40.3003 41.5787 42.2179 42.5375 1.41704 1.40287 1.41573 4.23577 106.094 +20240125_175029__terrains__results__num_matched10__maxframelookahead0__760p__unified_sift 42 163 11.1608 0.2 / 1.1 0.5 / 2.4 0.5 / 2.4 8.1629 10.2751 90 84 6 0.2 / 0.5 0.5 / 1.4 0.5 / 0.7 0.00882673 0.3 / 1.0 0.6 / 0.7 0.771187 0.749274 0.0112698 4.07584 0.204777 4.28062 17 474 3.0 / 3.5 0.2 / 0.3 0.2 / 0.2 0.3 / 0.3 0.2 / 0.3 28.3674 35.6327 38.0544 39.2653 39.8708 0.0651553 0.0674355 0.0623069 0.194981 60.6998 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0.5 0.2 / 0.5 0.2 / 0.2 0.0137587 0.1 / 0.2 0.1 / 0.2 2.05479 2.01459 0.0122871 8.13439 1.34809 9.48248 30 4066 4.0 / 4.7 0.5 / 0.6 0.1 / 0.1 0.1 / 0.2 0.1 / 0.1 62.8427 67.9942 69.7114 70.57 70.9993 1.63449 1.6264 1.62307 4.88405 129.616 +20240125_175029__terrains__results__num_matched10__maxframelookahead10__760p__unified_loftr 42 407 11.2084 0.3 / 2.9 0.6 / 7.1 0.7 / 7.3 13.3342 27.2741 197 176 21 0.3 / 0.7 0.5 / 2.1 0.1 / 0.1 0.0120163 0.2 / 0.2 0.1 / 0.2 0.90264 0.852765 0.00600743 11.9578 2.60205 14.5598 18 8532 3.0 / 3.7 0.5 / 0.6 0.1 / 0.1 0.1 / 0.2 0.1 / 0.1 36.3755 40.2645 41.5608 42.209 42.5331 1.68377 1.69633 1.6523 5.03251 104.952 +20240125_175029__terrains__results__num_matched10__maxframelookahead10__760p__unified_sift 42 407 11.5488 0.2 / 1.9 0.5 / 2.6 0.5 / 3.0 8.31394 11.8236 105 98 7 0.2 / 0.6 0.5 / 1.3 0.5 / 0.7 0.00846863 0.3 / 1.0 0.6 / 0.7 0.742582 0.721321 0.0121245 3.74264 0.218655 3.96129 17 474 3.0 / 3.5 0.2 / 0.3 0.2 / 0.2 0.3 / 0.3 0.2 / 0.3 28.3674 35.6327 38.0544 39.2653 39.8708 0.0739474 0.0698287 0.065778 0.209653 62.3358 +20240125_175029__terrains__results__num_matched10__maxframelookahead10__760p__unified_superglue 42 407 11.1023 0.1 / 1.9 0.2 / 0.9 0.2 / 2.0 11.3815 58.8779 247 243 4 0.1 / 0.4 0.2 / 0.4 0.3 / 0.3 0.0144179 0.1 / 0.2 0.2 / 0.3 2.11765 2.06603 0.0196745 7.97975 1.31119 9.29094 30 4142 4.0 / 4.7 0.5 / 0.6 0.1 / 0.1 0.1 / 0.2 0.1 / 0.1 60.9671 67.244 69.3363 70.3824 70.9055 1.50234 1.47828 1.46632 4.44704 127.761 +20240125_175029__terrains__results__num_matched5__maxframelookahead0__760p__unified_disk 42 131 11.2569 0.1 / 0.2 0.2 / 0.3 0.2 / 0.4 34.8583 120.683 118 114 4 0.1 / 0.2 0.2 / 0.3 0.1 / 0.1 0.0114725 0.2 / 0.2 0.1 / 0.4 0.793235 0.723463 0.020282 17.2039 3.20288 20.4068 18 11176 4.0 / 4.8 0.5 / 0.6 0.0 / 0.0 0.1 / 0.1 0.0 / 0.0 40.2454 41.8124 42.3348 42.596 42.7266 4.22303 4.19942 4.12502 12.5476 237.744 +20240125_175029__terrains__results__num_matched5__maxframelookahead0__760p__unified_lightglue 42 131 11.3026 0.1 / 0.1 0.2 / 0.2 0.2 / 0.3 19.5426 45.9125 123 123 0 0.1 / 0.1 0.2 / 0.2 0.1 / 0.1 0.00675583 0.1 / 0.1 0.1 / 0.2 0.878736 0.834702 0.0156641 6.47456 1.00389 7.47846 18 3406 4.0 / 4.7 0.5 / 0.6 0.0 / 0.0 0.1 / 0.1 0.0 / 0.0 40.6797 41.9862 42.4216 42.6394 42.7483 1.05105 1.04227 1.04856 3.14199 117.433 +20240125_175029__terrains__results__num_matched5__maxframelookahead0__760p__unified_loftr 42 131 11.2221 0.2 / 0.9 0.4 / 1.8 0.4 / 1.9 42.5815 25.0904 119 116 3 0.2 / 0.5 0.4 / 0.9 0.1 / 0.1 0.00470138 0.2 / 0.3 0.1 / 0.2 1.06786 1.01815 0.00697184 10.4356 2.25987 12.6954 18 6904 3.0 / 3.6 0.4 / 0.5 0.2 / 0.2 0.2 / 0.3 0.1 / 0.2 33.9825 39.3073 41.0822 41.9697 42.4134 1.12711 1.13801 1.17241 3.43765 126.964 +20240125_175029__terrains__results__num_matched5__maxframelookahead0__760p__unified_sift 42 131 15.8114 0.2 / 1.2 0.5 / 2.4 0.5 / 2.4 12.8267 9.84406 88 82 6 0.2 / 0.5 0.5 / 1.4 0.5 / 0.7 0.00838614 0.3 / 1.0 0.6 / 0.6 0.716169 0.690338 0.011492 4.01306 0.234138 4.2472 17 472 3.0 / 3.5 0.2 / 0.3 0.2 / 0.2 0.3 / 0.3 0.2 / 0.2 28.4488 35.6652 38.0707 39.2735 39.8748 0.0729437 0.0676634 0.062696 0.203378 68.8362 +20240125_175029__terrains__results__num_matched5__maxframelookahead0__760p__unified_superglue 42 131 11.4986 0.1 / 0.1 0.1 / 0.2 0.2 / 0.2 22.8721 44.8608 123 123 0 0.1 / 0.1 0.1 / 0.2 0.0 / 0.0 0.00655961 0.1 / 0.1 0.0 / 0.1 0.969332 0.941203 0.00511885 6.27453 1.02098 7.29551 18 3446 4.0 / 4.7 0.5 / 0.6 0.0 / 0.0 0.0 / 0.1 0.0 / 0.0 40.3255 41.8445 42.3508 42.604 42.7306 1.16597 1.17949 1.18278 3.52834 119.647 +20240125_175029__terrains__results__num_matched5__maxframelookahead10__760p__unified_disk 42 389 11.0206 0.2 / 0.9 0.2 / 1.4 0.2 / 1.6 31.9792 150.822 215 210 5 0.1 / 0.4 0.2 / 0.5 0.5 / 0.4 0.0131631 0.2 / 0.3 0.3 / 0.5 2.41158 2.29934 0.0385406 24.0524 4.49673 28.5491 30 14933 4.0 / 4.6 0.5 / 0.6 0.1 / 0.1 0.1 / 0.2 0.1 / 0.1 62.6845 67.9309 69.6797 70.5542 70.9914 6.79726 6.89381 6.96413 20.6553 287.844 +20240125_175029__terrains__results__num_matched5__maxframelookahead10__760p__unified_lightglue 42 389 11.1506 0.1 / 0.7 0.2 / 0.7 0.2 / 0.9 17.9343 59.0549 223 220 3 0.1 / 0.4 0.2 / 0.4 0.2 / 0.2 0.0147309 0.1 / 0.2 0.2 / 0.2 2.05416 2.00123 0.0236316 8.66348 1.37235 10.0358 30 4202 4.0 / 4.7 0.5 / 0.6 0.1 / 0.1 0.1 / 0.2 0.1 / 0.1 62.513 67.8623 69.6455 70.537 70.9828 1.45956 1.46807 1.48223 4.40996 135.689 +20240125_175029__terrains__results__num_matched5__maxframelookahead10__760p__unified_loftr 42 389 14.9301 0.3 / 3.4 0.5 / 6.1 0.6 / 6.4 74.8316 28.4682 183 160 23 0.3 / 0.6 0.5 / 1.1 0.2 / 0.2 0.0080936 0.2 / 0.3 0.1 / 0.2 1.00379 0.955474 0.0054462 11.0543 2.35855 13.4129 18 7273 3.0 / 3.6 0.4 / 0.5 0.2 / 0.2 0.2 / 0.4 0.1 / 0.2 32.5812 38.6496 40.7534 41.8053 42.3312 1.22466 1.19551 1.20104 3.62132 168.327 +20240125_175029__terrains__results__num_matched5__maxframelookahead10__760p__unified_sift 42 389 11.117 0.2 / 1.9 0.5 / 2.6 0.5 / 3.0 9.40594 11.6687 104 97 7 0.2 / 0.6 0.5 / 1.3 0.5 / 0.7 0.00861669 0.3 / 1.0 0.6 / 0.7 0.84395 0.807965 0.0235164 3.86538 0.200752 4.06614 17 472 3.0 / 3.5 0.2 / 0.3 0.2 / 0.2 0.3 / 0.3 0.2 / 0.3 28.3562 35.6282 38.0522 39.2642 39.8702 0.065959 0.0691495 0.0692508 0.204441 62.7563 +20240125_175029__terrains__results__num_matched5__maxframelookahead10__760p__unified_superglue 42 389 11.1268 0.1 / 2.0 0.2 / 0.9 0.2 / 2.1 29.5523 57.9483 231 227 4 0.1 / 0.3 0.2 / 0.3 0.3 / 0.3 0.0142558 0.1 / 0.2 0.2 / 0.3 2.34799 2.29819 0.0179238 9.96332 1.40571 11.369 30 4251 4.0 / 4.7 0.5 / 0.6 0.1 / 0.1 0.1 / 0.2 0.1 / 0.1 62.1691 67.7248 69.5767 70.5026 70.9656 1.67547 1.63619 1.68819 4.99998 149.25 \ No newline at end of file diff --git a/creta_bata.py b/creta_bata.py new file mode 100644 index 000000000..42bb4ccb9 --- /dev/null +++ b/creta_bata.py @@ -0,0 +1,427 @@ +import numpy as np +import networkx as nx +import h5py +from scipy.linalg import svd +from scipy.sparse import csr_matrix +from scipy.sparse.linalg import spsolve + + +def RunTA(edges, RT, T_avg, maxImages, param, TAMethod="BATA"): + # Relabelling nodes + print(edges.shape) + nodes = np.unique(edges - 1).astype(int) + print(nodes.shape) + G = nx.Graph() + G.add_weighted_edges_from(np.hstack((edges, np.arange(edges.shape[0]).reshape((-1, 1))))) + D = dict(G.degree()) + loc = max(D, key=D.get) + idxConst = np.where(nodes == loc)[0][0] + G1 = G.subgraph(nodes) + P = G1.edges + edges_sg = np.array(P) + idx = np.where(edges[:, 0] > edges[:, 1])[0] + edge_idx_map = np.array([int(d["weight"]) for (_, _, d) in G1.edges(data=True)]) + RT[:, idx] = -RT[:, idx] + RT_sg = RT[:, edge_idx_map] + + if len(T_avg) == 0: + C_avg_temp = np.array([]) + else: + C_avg_temp = T_avg[:, nodes] + + # Translation Averaging + # if TAMethod == 'RLUD': + # C_avg_temp, ed_ret_idx = RLUD(edges_sg.T, -RT_sg, param, C_avg_temp) + # tacost = LUDCost(RT_sg, edges_sg, C_avg_temp) + if TAMethod == 'BATA': + C_avg_temp, ed_ret_idx = BATA(edges_sg.T, -RT_sg, param, C_avg_temp, idxConst) + tacost = BATACost(RT_sg, edges_sg, C_avg_temp) + else: + raise ValueError('Invalid TA method') + + # Converting back to original labels + T_avg = np.full((3, maxImages), np.nan) + T_avg[:, nodes] = C_avg_temp + ed_ret_idx[edge_idx_map] = ed_ret_idx + + return T_avg, tacost, ed_ret_idx + +def RLUD(edges_sg, RT_sg, param, C_avg_temp): + # RLUD implementation + # (Implement RLUD function according to your needs) + # This function should update C_avg_temp and return ed_ret_idx + # based on the RLUD translation averaging method. + # ... + pass + +def LUDCost(RT_sg, edges_sg, C_avg_temp): + # LUDCost implementation + # (Implement LUDCost function according to your needs) + # This function should calculate and return the cost of + # translation averaging based on the RLUD method. + # ... + pass + +def BATACost(RT, edges, T): + # This function determines BATA cost for a given solution T + # Inputs: + # RT: 3XN matrix of (Cj-Ci)/norm(Cj-Ci) + # edges: MX2 matrix of edge + # T: 3XN matrix of camera locations + # Outputs: + # cost: Mean error of all the edge costs + + T_Diff = T[:, edges[:, 1]] - T[:, edges[:, 0]] + T_DiffNorm = np.sum(T_Diff * RT, axis=0) / np.sum(T_Diff * T_Diff, axis=0) + T_DiffNorm[T_DiffNorm < 0] = 0 + costall = np.linalg.norm(T_Diff * T_DiffNorm - RT, axis=0, ord=2) + costall = costall[~np.isnan(costall)] + cost = np.mean(costall) + + return cost + +def BATA(tij_index, tij_observe, param, t_init_given=None, idxConst=None): + if t_init_given is None: + t_init_given = np.empty((3, 0)) + + numofcam = int(np.max(tij_index) + 1) + numofobser = int(tij_observe.shape[1]) + Ri_T = np.tile(np.eye(3), numofobser).T + Rj_T = Ri_T.copy() + + index_ti_I = np.column_stack((np.arange(0, 3 * numofobser), + np.arange(0, 3 * numofobser), + np.arange(0, 3 * numofobser))).astype(int) + + index_ti_J = (tij_index[1] - 1) * 3 + index_ti_J = np.concatenate((index_ti_J, index_ti_J + 1, index_ti_J + 2)) + index_ti_J = np.tile(index_ti_J, (3, 1)).T.astype(int) + + index_tj_I = np.column_stack((np.arange(0, 3 * numofobser), + np.arange(0, 3 * numofobser), + np.arange(0, 3 * numofobser))).astype(int) + + index_tj_J = (tij_index[1] - 1) * 3 + index_tj_J = np.concatenate((index_tj_J, index_tj_J + 1, index_tj_J + 2)) + index_tj_J = np.tile(index_tj_J, (3, 1)).T.astype(int) + + At0_full = csr_matrix((Ri_T.T.flatten(), (index_ti_I.flatten(), index_ti_J.flatten())), + shape=(3 * numofobser, 3 * numofcam)) - \ + csr_matrix((Rj_T.T.flatten(), (index_tj_I.flatten(), index_tj_J.flatten())), + shape=(3 * numofobser, 3 * numofcam)) + + Z = np.zeros((4, 4)) + if t_init_given.size == 0: + Aeq1 = np.sum(np.tile(tij_observe, (1, 3)).reshape(-1, numofobser) * At0_full, axis=0) + Aeq2 = np.eye(3) + Aeq = np.vstack([Aeq1, Aeq2]) + beq1 = numofobser + beq2 = np.zeros(3) + beq = np.hstack([beq1, beq2]) + + # Initialization with LUDRevised + Svec = np.random.rand(numofobser) + 0.5 + Svec = Svec / np.sum(Svec) * numofobser + S = np.repeat(Svec, 3) + W = np.ones(3 * numofobser) + ii = 1 + + errPrev = 1 + errCurr = 0 + while ii <= param['numofiterinit'] and abs(errPrev - errCurr) / errPrev > 1e-5: + A = csr_matrix(np.diag(W)) * At0_full + B = W * S * tij_observe.flatten() + X = np.linalg.lstsq(np.vstack([A.T @ A, Aeq.T, np.hstack([Aeq, Z])]), + np.hstack([A.T @ B, beq]), rcond=None)[0] + t = X[:3 * numofcam] + Aij = At0_full @ t + tij_T = tij_observe.flatten() + Svec = np.sum(Aij * tij_T.reshape(-1, 1), axis=0) / np.sum(tij_T ** 2, axis=0) + Svec[Svec < 0] = 0 + tmp3 = np.tile(Svec, 3) + S = tmp3 + errPrev = errCurr + tmp = np.sum((Aij - S * tij_T.reshape(-1, 1)) ** 2, axis=0) + Wvec = 1 / (1 + (tmp / param['delta']) ** 2) + errCurr = np.sum(np.sqrt(tmp)) + W = np.tile(np.sqrt(Wvec), 3) + ii += 1 + else: + t = t_init_given.flatten() + + # RLUD init + ii = 1 + node_ret_idx = np.ones((3, numofcam), dtype=bool) + errPrev = 1 + errCurr = 0 + beq2 = np.zeros(3) + I = np.eye(3) + + while ii <= param['numofouteriter'] and abs(errPrev - errCurr) / errPrev > 1e-5: + Aij = At0_full @ t + tij_T = tij_observe.flatten() + Svec = np.sum(Aij.reshape(3, -1) * tij_T.reshape(3, -1), axis=0) / \ + np.sum(tij_T.reshape(3, -1) ** 2, axis=0) + print("Svec", Svec) + + ed_ret_idx, node_ret_idx = extractLargestConnComp(Svec, tij_index, node_ret_idx) + numEdgesRet = np.sum(ed_ret_idx[0, :]) + numNodesRet = np.sum(node_ret_idx[0, :]) + + t2 = t[node_ret_idx.flatten()] + + Svec = Svec[ed_ret_idx.flatten()] + At0_fullr = At0_full[ed_ret_idx.flatten(), :][:, node_ret_idx.flatten()] + + tij_T = tij_observe[:, ed_ret_idx.flatten()] + tmp3 = np.tile(Svec, 3) + S_red = tmp3 + Wvec2 = Wvec[ed_ret_idx.flatten()] + A = csr_matrix(np.diag(Wvec2)) * At0_fullr + B = Wvec2 * Svec * tij_T.flatten() + Aeq1 = np.sum(tij_T * At0_fullr, axis=0) + Aeq2 = np.tile(I, numNodesRet) + X = np.linalg.lstsq(np.vstack([A.T @ A, Aeq1, Aeq2.T]), + np.hstack([A.T @ B, np.sum(tij_T, axis=1), beq2]), rcond=None)[0] + t[node_ret_idx.flatten()] = X[:numNodesRet] + ii += 1 + + # BATA + t = t - np.tile(t[3 * idxConst - 2:3 * idxConst], numofcam) + node_ret_idx_temp = np.ones((3, numofcam), dtype=bool) + node_ret_idx_temp[:, idxConst - 1] = False + t = t[node_ret_idx_temp.flatten()] + At0 = At0_full[:, node_ret_idx_temp.flatten()] + + errPrev = 1 + errCurr = 0 + ii = 1 + + while ii <= param['numofouteriter'] and abs(errCurr - errPrev) / errPrev > 1e-5: + Aij = At0 @ t + tij_T_weighted = tij_observe.flatten() + Svec = np.sum(Aij ** 2, axis=0) / np.sum(Aij * tij_T_weighted.reshape(3, -1), axis=0) + tmp3 = np.tile(Svec, 3) + S = tmp3 + + A = csr_matrix(np.diag(1 / S)) * At0 + B = tij_observe.flatten() + tmp = np.sqrt(np.sum((A @ t - B) ** 2, axis=0)) + errPrev = errCurr + errCurr = np.sum(tmp[ed_ret_idx.flatten()]) + Wvec = 1 / (1 + ((tmp / param['robustthre']) ** 2)) + + jj = 1 + while jj <= param['numofinneriter']: + Aij = At0 @ t + tij_T_weighted = tij_observe.flatten() + Svec = np.sum(Aij ** 2, axis=0) / np.sum(Aij * tij_T_weighted.reshape(3, -1), axis=0) + + ed_ret_idx, node_ret_idx_temp = extractLargestConnComp(Svec, tij_index, node_ret_idx) + node_ret_idx_temp2 = node_ret_idx_temp[:, np.setdiff1d(np.arange(node_ret_idx_temp.shape[1]), idxConst - 1)] + Svec = Svec[ed_ret_idx.flatten()] + tmp3 = np.tile(Svec, 3) + S_red = tmp3 + Wvec2 = Wvec[ed_ret_idx.flatten()] + At0_fullr = At0_full[ed_ret_idx.flatten(), :][:, node_ret_idx_temp2.flatten()] + tij_T = tij_observe[:, ed_ret_idx.flatten()] + W = np.tile(np.sqrt(Wvec2), 3) + + A = csr_matrix(np.diag(W / S_red)) * At0_fullr + B = W * tij_T.flatten() + t2 = spsolve(A.T @ A, A.T @ B) + t[node_ret_idx_temp2.flatten()] = t2 + jj += 1 + + ii += 1 + + t = t.reshape(3, -1) + t[:, :idxConst - 1] = t[:, np.arange(idxConst - 1) - 1] + t[:, idxConst - 1:] = t[:, np.arange(idxConst - 1, t.shape[1] - 1)] + t[~node_ret_idx.flatten()] = np.nan + t = t - np.nanmean(t, axis=1, keepdims=True) + nodes = np.where(node_ret_idx[0, :])[0] + ed_ret_idx = np.isin(tij_index[0, :], nodes) & np.isin(tij_index[1, :], nodes) + + return t, ed_ret_idx + + +def extractLargestConnComp(Svec, tij_index, node_ret_idx): + ed_ret_idx = np.ones((3, tij_index.shape[1]), dtype=bool) + ed_ret_idx[:, Svec < 0] = False + nodes = np.where(node_ret_idx[0])[0] + eidx = np.isin(tij_index[0], nodes) & np.isin(tij_index[1], nodes) + ed_ret_idx[:, ~eidx] = False + edges = tij_index[:, ed_ret_idx[0]] + + G = nx.Graph() + G.add_edges_from(edges.T) + bins = list(nx.connected_components(G)) + largest_comp = np.array(list(max(bins, key=len))).astype(int) + ed_ret_idx_temp = np.isin(tij_index[0], largest_comp) & np.isin(tij_index[1], largest_comp) & ed_ret_idx[0] + ed_ret_idx = np.tile(ed_ret_idx_temp, (3, 1)) + node_ret_idx[...] = False + node_ret_idx[:, largest_comp] = True + + return ed_ret_idx, node_ret_idx + + + +extractParallelRigidSubgraph = True +extractParallelRigidSubgraphIter = False +loadGTComponent = False +loadRavgFilteredGraph = True + +computeInitialRelTransIRLS = False +computeInitialRelTransRANSAC = True + +stepNormConvTol = 1e-6 +TACostRelConvTol = 1e-5 + +TAMethod = 'BATA' + +# Translation Averaging Parameters +param = {} +if TAMethod == 'RLUD': + maxIters = 50 + # RLUD Parameters + delta = 1e-5 + numofiterinit = 50 + numofouteriter = 20 + robustthre = 1e-1 +elif TAMethod == 'BATA': + maxIters = 10 + # Parameters for RLUD-BATA + param["delta"] = 1e-5 + param["numofiterinit"] = 50 + param["numofouteriter"] = 20 + param["numofinneriter"] = 5 + param["robustthre"] = 1e-1 +else: + raise ValueError('Invalid TA method') + +# Relative translation estimation parameters +SIGMA = 1e-2 + +# Filter Edges in Iteration +maxAngleDeg = 40 + +# Load data +#data = scipy.io.loadmat('dummy_data.mat') +f = h5py.File('dummy_data.mat','r') +print(list(f.keys())) +data = {"RT": f["RT"].value.T, "R_avg": f.get("R_avg").value.T, "edges": f.get("edges").value.T, "matches": f.get("matches").value, "maxImages": int(f.get("maxImages").value[0][0])} +print(data["R_avg"].shape) +matches = [] +for _mtch in data["matches"].flatten(): + name = h5py.h5r.get_name(_mtch, f.id) + matches.append(f[name].value.T) +data["matches"] = matches +# The data should contain the following variables: +# Graph has N nodes and M edges. +# RT: 3XM matrix of relative translation directions (Tij=Rj*(Ti-Tj)). +# edges: Mx2 matrix of camera pairs. +# matches: Mx1 list each containing Kx4 matrix. +# Each matrix contains the point correspondences for each edge. +# Each row in the matrix contains (xi,yi,xj,yj) where xi,yi and xj,yj +# are coordinates for images i,j respectively after correcting for camera intrinsics. +# R_avg: 3X3XN matrix of absolute rotations. +# maxImages: Maximum No. of images in the dataset (used for indexing) +# NOTE: Ensure that the graph is connected and has the maximal parallel +# rigid component. + +# Compute unit norm feature vectors +for i in range(len(data['matches'])): + tempMat = data['matches'][i] + numInliers = tempMat.shape[0] + x1 = np.concatenate([tempMat[:, :2], np.ones((numInliers, 1))], axis=1) + x1 = x1 / np.linalg.norm(x1, axis=1, keepdims=True) + x2 = np.concatenate([tempMat[:, 2:4], np.ones((numInliers, 1))], axis=1) + x2 = x2 / np.linalg.norm(x2, axis=1, keepdims=True) + data['matches'][i] = np.concatenate([x1, x2], axis=1) + +# Pre-processing matches +for i in range(len(data['matches'])): + tempMat = data['matches'][i] + tempMat[:, :3] = tempMat[:, :3] @ data['R_avg'][:, :, int(data['edges'][i, 0] - 1)] + tempMat[:, 3:] = tempMat[:, 3:] @ data['R_avg'][:, :, int(data['edges'][i, 1] - 1)] + data['matches'][i] = np.cross(tempMat[:, 3:], tempMat[:, :3]) + +# Pre-processing relative translations +for i in range(data['edges'].shape[0]): + data['RT'][:, i] = -data['R_avg'][:, :, int(data['edges'][i, 1] - 1)].T @ data['RT'][:, i] + +# CReTA +iter = 1 +stepNorm = 1 +tacostprev = 100 +tacost = 1 +print(data['maxImages']) +T_avg = np.array([]) + +while (iter <= maxIters and stepNorm > stepNormConvTol and abs(tacostprev - tacost) / tacostprev > TACostRelConvTol): + # Run Translation Averaging + C_avg_prev = T_avg.copy() + tacostprev = tacost + T_avg, tacost, ed_ret_idx = RunTA(data['edges'], data['RT'], T_avg, data['maxImages'], param) + + # Remove edges that were filtered out + eidx = ~ed_ret_idx + data['edges'] = np.delete(data['edges'], eidx, axis=0) + data['RT'] = np.delete(data['RT'], eidx, axis=1) + data['matches'] = [data['matches'][i] for i in range(len(data['matches'])) if not eidx[i]] + + # Filter Edges in iteration + RT_camSol = T_avg[:, data['edges'][:, 1]] - T_avg[:, data['edges'][:, 0]] + RT_camSol = RT_camSol / np.linalg.norm(RT_camSol, axis=0) + dotProduct = np.sum(data['RT'] * RT_camSol, axis=0) + angles = np.abs(np.arccos(dotProduct)) + SolCalcDiffAngles = angles * 180 / np.pi + + eidx = SolCalcDiffAngles > maxAngleDeg + data['edges'] = np.delete(data['edges'], eidx, axis=0) + data['RT'] = np.delete(data['RT'], eidx, axis=1) + data['matches'] = [data['matches'][i] for i in range(len(data['matches'])) if not eidx[i]] + + # Extract largest connected component + G = nx.Graph() + G.add_edges_from(data['edges']) + bins = list(nx.connected_components(G)) + nodes = max(bins, key=len) # Map from new (idx) to old (value) + + eidxRet = np.isin(data['edges'][:, 0], list(nodes)) & np.isin(data['edges'][:, 1], list(nodes)) + eidx = ~eidxRet + data['edges'] = np.delete(data['edges'], eidx, axis=0) + data['RT'] = np.delete(data['RT'], eidx, axis=1) + data['matches'] = [data['matches'][i] for i in range(len(data['matches'])) if not eidx[i]] + + # Compute relative translations + for i in range(data['edges'].shape[0]): + tij = (T_avg[:, data['edges'][i, 1]] - T_avg[:, data['edges'][i, 0]]) + tij = tij / np.linalg.norm(tij) + tempMat = data['matches'][i] + err_vec = np.abs(tempMat @ tij) + # Remove points at 1st iteration + if iter == 1: + s_err = np.sort(err_vec) + remPtsIdx = err_vec > s_err[int(np.ceil(len(s_err) * 0.75))] + err_vec = np.delete(err_vec, remPtsIdx) + tempMat = np.delete(tempMat, remPtsIdx, axis=0) + data['matches'][i] = tempMat + w_vec = 1 / (1 + (err_vec / SIGMA) ** 2) + _, _, v = svd(tempMat.T @ (w_vec * tempMat)) + tij_ref = v[:, -1] + if tij @ tij_ref < 0: + tij_ref = -tij_ref + data['RT'][:, i] = tij_ref + + # Update parameters + if iter > 1: + stepDiff = C_avg_prev - T_avg + stepDiff = np.delete(stepDiff, np.any(np.isnan(stepDiff), axis=0), axis=1) + stepNorm = np.mean(np.linalg.norm(stepDiff, axis=0)) + print(f'Iteration: {iter} done') + iter += 1 + + + diff --git a/dummy_data.mat b/dummy_data.mat new file mode 100644 index 0000000000000000000000000000000000000000..1e422fa7d0df481df54c436537e1218870e290d6 GIT binary patch literal 2763754 zcmeFXbxdVj(>I7T(73z1HSXTHJ2dX@?$$Uo?$EfqySux)9^BmzOyB3;`z7C($@|wN zlgT`FvQt&7Z13M%yOO=mk`+{u5fmcgpl2bH6;z=!wXin%Mx4wuiJ6;)k(-H`h?$X@nMguJjE%_2$lS!n zfQX5nk&%d==>LvD{v|L;*?-0V^8a@b2>&x@@Gv+q|Ed1~Hq@51rD| z{Ab`_{ckG>i2gqU=s!H{A71iLJSfOtKM)YCf5wsj$^U=)sVIqxg8cFPH~+u-XPv`q$WB1Pb!?Z`(f(pdeI#kAwX^Fa`b} z733cV@_*{!e^&lqp2$Q3e=_|=-$7VmL0CWk34r(~KN$Gm31LBq{^Jk&zv}%}9~8vc 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a/gtsfm/averaging/translation/averaging_creta.py b/gtsfm/averaging/translation/averaging_creta.py new file mode 100644 index 000000000..c371ccb1a --- /dev/null +++ b/gtsfm/averaging/translation/averaging_creta.py @@ -0,0 +1,119 @@ +import os +import time +from typing import Dict, List, Optional, Tuple + +import numpy as np +from scipy.io import savemat, loadmat +from gtsam import Pose3, Rot3, Unit3, Point3 + +import gtsfm.common.types as gtsfm_types +import gtsfm.utils.logger as logger_utils +import gtsfm.utils.features as feature_utils +import gtsfm.utils.cache as cache_utils +from gtsfm.averaging.translation.translation_averaging_base import TranslationAveragingBase +from gtsfm.common.pose_prior import PosePrior +from gtsfm.common.sfm_track import SfmTrack2d +from gtsfm.common.keypoints import Keypoints +from gtsfm.evaluation.metrics import GtsfmMetric, GtsfmMetricsGroup +from gtsfm.averaging.translation.translation_averaging_base import TranslationAveragingBase +from gtsfm.averaging.translation.averaging_1dsfm import get_valid_measurements_in_world_frame, compute_metrics + + +logger = logger_utils.get_logger() + + +class TranslationAveragingCReTA(TranslationAveragingBase): + + def run_translation_averaging( + self, + num_images: int, + i2Ui1_dict: Dict[Tuple[int, int], Optional[Unit3]], + wRi_list: List[Optional[Rot3]], + corr_idxs_dict: Dict[Tuple[int, int], np.ndarray], + keypoints_list: List[Keypoints], + tracks_2d: Optional[List[SfmTrack2d]] = None, + intrinsics: Optional[List[Optional[gtsfm_types.CALIBRATION_TYPE]]] = None, + absolute_pose_priors: List[Optional[PosePrior]] = [], + i2Ti1_priors: Dict[Tuple[int, int], PosePrior] = {}, + scale_factor: float = 1.0, + gt_wTi_list: List[Optional[Pose3]] = [], + ) -> Tuple[List[Optional[Pose3]], Optional[GtsfmMetricsGroup], Optional[List[Tuple[int, int]]]]: + """Run the translation averaging, and combine the estimated global translations with global rotations. + + Args: + num_images: Number of camera poses. + i2Ui1_dict: Relative unit-trans as dictionary (i1, i2): i2Ui1. + wRi_list: Global rotations for each camera pose in the world coordinates. + tracks_2d: 2d tracks. + intrinsics: List of camera intrinsics. + absolute_pose_priors: Priors on the camera poses (not delayed). + i2Ti1_priors: Priors on the pose between camera pairs (not delayed) as (i1, i2): i2Ti1. + scale_factor: Non-negative global scaling factor. + gt_wTi_list: List of ground truth poses (wTi) for computing metrics. + + Returns: + Global camera poses wTi. The number of entries in the list is `num_images`. The list + may contain `None` where the global translations could not be computed (either underconstrained system + or ill-constrained system). + A GtsfmMetricsGroup with translation averaging metrics. + Indices of inlier measurements (list of camera pair indices). + """ + # Generate CReTA inputs. + """[From CReTA_demo.m] + The data should contain the following variables: + Graph has N nodes and M edges. + RT: 3XM matrix of relative translation directions (Tij=Rj*(Ti-Tj)). [i2Ui1 in our notation] + edges: Mx2 matrix of camera pairs.RT + matches: Mx1 cell each containing Kx4 matrix. + Each matrix contains the point correspondences for each edge. + Each row in the matrix contains (xi,yi,xj,yj) where xi,yi and xj,yj + are coordinates for images i,j respectively afer correcting for camera intrinsics. + R_avg: 3X3XN matrix of absolute rotations. [iRw in our notation] + maxImages: Maimum No. of images in the dataset (used for indexing) + NOTE: Ensure that the graph is connected and has the maximal parallel + rigid component. + """ + w_i2Ui1_dict, valid_cameras = get_valid_measurements_in_world_frame(i2Ui1_dict, wRi_list) + RT = np.array([i2Ui1.point3().flatten() for i2Ui1 in i2Ui1_dict.values()]).T + R_avg = np.array([wRi_list[ii].matrix() for ii in valid_cameras]).T + + edges = np.array([(i1, i2) for i1, i2 in i2Ui1_dict.keys()]) + matches = [] + for (i1, i2), i2Ui1 in i2Ui1_dict.items(): + if i2Ui1 is None: + continue + # Calibrate keypoints. + uv_norm_i1 = feature_utils.normalize_coordinates(keypoints_list[i1].coordinates, intrinsics[i1]) + uv_norm_i2 = feature_utils.normalize_coordinates(keypoints_list[i2].coordinates, intrinsics[i2]) + + corr = corr_idxs_dict[(i1, i2)] + matches_i12i = np.hstack((uv_norm_i1[corr[:, 0]], uv_norm_i2[corr[:, 1]])) + matches.append(matches_i12i) + matches_hash = cache_utils.generate_hash_for_numpy_array(np.concatenate(matches)) + + # Write inputs for MATLAB script. + savemat(f"ta_input_{matches_hash}.mat", {"RT": RT, "edges": edges, "R_avg": R_avg, "matches": matches}) + if not os.path.isfile(f"ta_output_{matches_hash}.mat"): + raise FileExistsError("Could not find CReTA translation averaging solution.") + + # Read in MATLAB results. + res = loadmat(f"ta_output_{matches_hash}.mat") + wti_list = [Point3(res["T_avg"][:, i].flatten()) for i in range(res["T_avg"].shape[1])] + inlier_edges = [(int(i1 - 1), int(i2 - 1)) for i1, i2 in res["edges"]] + + # Compute the metrics. + ta_metrics = compute_metrics(set(inlier_edges), i2Ui1_dict, wRi_list, wti_list, gt_wTi_list) + + num_translations = sum([1 for wti in wti_list if wti is not None]) + logger.info("Estimated %d translations out of %d images.", num_translations, num_images) + + # Combine estimated global rotations and global translations to Pose(3) objects. + wTi_list = [ + Pose3(wRi, wti) if wRi is not None and wti is not None else None for wRi, wti in zip(wRi_list, wti_list) + ] + logger.info("Translation averaging took %.4f seconds.", 0.0) + ta_metrics.add_metric(GtsfmMetric("total_duration_sec", 0.0)) + ta_metrics.add_metric(GtsfmMetric("outlier_rejection_duration_sec", 0.0)) + ta_metrics.add_metric(GtsfmMetric("optimization_duration_sec", 0.0)) + + return wTi_list, ta_metrics, inlier_edges diff --git a/gtsfm/averaging/translation/translation_averaging_base.py b/gtsfm/averaging/translation/translation_averaging_base.py index 87b02c95e..6bf55504f 100644 --- a/gtsfm/averaging/translation/translation_averaging_base.py +++ b/gtsfm/averaging/translation/translation_averaging_base.py @@ -85,6 +85,8 @@ def create_computation_graph( num_images: int, i2Ui1_graph: Delayed, wRi_graph: Delayed, + corr_idxs_dict: Delayed, + keypoints_list: Delayed, tracks_2d: Optional[Delayed] = None, intrinsics: Optional[List[Optional[gtsfm_types.CALIBRATION_TYPE]]] = None, absolute_pose_priors: List[Optional[PosePrior]] = [], @@ -114,6 +116,8 @@ def create_computation_graph( num_images=num_images, i2Ui1_dict=i2Ui1_graph, wRi_list=wRi_graph, + corr_idxs_dict=corr_idxs_dict, + keypoints_list=keypoints_list, tracks_2d=tracks_2d, intrinsics=intrinsics, absolute_pose_priors=absolute_pose_priors, diff --git a/gtsfm/configs/unified.yaml b/gtsfm/configs/unified.yaml index 8fef52586..98b899d65 100644 --- a/gtsfm/configs/unified.yaml +++ b/gtsfm/configs/unified.yaml @@ -71,9 +71,8 @@ SceneOptimizer: _target_: gtsfm.averaging.rotation.shonan.ShonanRotationAveraging trans_avg_module: - _target_: gtsfm.averaging.translation.averaging_1dsfm.TranslationAveraging1DSFM + _target_: gtsfm.averaging.translation.averaging_creta.TranslationAveragingCReTA robust_measurement_noise: True - projection_sampling_method: SAMPLE_INPUT_MEASUREMENTS data_association_module: _target_: gtsfm.data_association.data_assoc.DataAssociation diff --git a/gtsfm/multi_view_optimizer.py b/gtsfm/multi_view_optimizer.py index d36244683..7892d7642 100644 --- a/gtsfm/multi_view_optimizer.py +++ b/gtsfm/multi_view_optimizer.py @@ -132,6 +132,8 @@ def create_computation_graph( num_images, pruned_i2Ui1_graph, delayed_wRi, + viewgraph_v_corr_idxs_graph, + keypoints_list, tracks2d_graph, all_intrinsics, absolute_pose_priors, diff --git a/scripts/benchmark_wildcat.sh b/scripts/benchmark_wildcat.sh index 12bd5f2ea..b10d59ec3 100755 --- a/scripts/benchmark_wildcat.sh +++ b/scripts/benchmark_wildcat.sh @@ -8,41 +8,41 @@ now=$(date +"%Y%m%d_%H%M%S") datasets=( # Tanks and Temples Dataset. - barn-tanks-and-temples-410 + # barn-tanks-and-temples-410 # Olsson Datasets. # See https://www.maths.lth.se/matematiklth/personal/calle/dataset/dataset.html - ecole-superieure-de-guerre-35 - fort-channing-gate-singapore-27 - skansen-kronan-gothenburg-131 - nijo-castle-gate-19 - kings-college-cambridge-328 - spilled-blood-cathedral-st-petersburg-781 - palace-fine-arts-281 - # 1dsfm Datasets - gendarmenmarkt-1463 - # Other. - skydio-crane-mast-501 - # Astrovision Datasets. - 2011205_rc3 - # Colmap Datasets. - south-building-128 + # ecole-superieure-de-guerre-35 + # fort-channing-gate-singapore-27 + # skansen-kronan-gothenburg-131 + # nijo-castle-gate-19 + # kings-college-cambridge-328 + # spilled-blood-cathedral-st-petersburg-781 + # palace-fine-arts-281 + # # 1dsfm Datasets + # gendarmenmarkt-1463 + # # Other. + # skydio-crane-mast-501 + # # Astrovision Datasets. + # 2011205_rc3 + # # Colmap Datasets. + # south-building-128 gerrard-hall-100 ) max_frame_lookahead_sizes=( - 0 - 5 + #0 + #5 10 - 15 + #15 ) num_matched_sizes=( - 0 + #0 5 - 10 - 15 - 20 - 25 + #10 + #15 + #20 + #25 ) correspondence_generator_config_names=( @@ -79,7 +79,7 @@ for num_matched in ${num_matched_sizes[@]}; do if [[ $correspondence_generator_config_name == *"sift"* ]] then - num_workers=10 + num_workers=1 elif [[ $correspondence_generator_config_name == *"lightglue"* ]] then num_workers=1 diff --git a/thirdparty/DKM b/thirdparty/DKM new file mode 160000 index 000000000..d1cba4bf1 --- /dev/null +++ b/thirdparty/DKM @@ -0,0 +1 @@ +Subproject commit d1cba4bf1c96de1497aba031f709013404c08d5f diff --git a/thirdparty/LightGlue b/thirdparty/LightGlue index fe7fb4fa0..29f3e449e 160000 --- a/thirdparty/LightGlue +++ b/thirdparty/LightGlue @@ -1 +1 @@ -Subproject commit fe7fb4fa0cffec65e33bf4c2f62a863d5b03433a +Subproject commit 29f3e449efa1994758b8a16299d2816028dca65b diff --git a/thirdparty/creta/BATA.m b/thirdparty/creta/BATA.m new file mode 100644 index 000000000..279691df1 --- /dev/null +++ b/thirdparty/creta/BATA.m @@ -0,0 +1,219 @@ +% Copyright (C) 2022 Computer Vision Lab, Electrical Engineering, +% Indian Institute of Science, Bengaluru, India. +% All rights reserved. +% +% Redistribution and use in source and binary forms, with or without +% modification, are permitted provided that the following conditions are +% met: +% +% * Redistributions of source code must retain the above copyright +% notice, this list of conditions and the following disclaimer. +% * Redistributions in binary form must reproduce the above +% copyright notice, this list of conditions and the following +% disclaimer in the documentation and/or other materials provided +% with the distribution. +% * Neither the name of Indian Institute of Science nor the +% names of its contributors may be used to endorse or promote products +% derived from this software without specific prior written permission. +% +% THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +% OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +% FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL +% THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +% LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +% FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER +% DEALINGS IN THE SOFTWARE. +% +% Author: Lalit Manam +% +% This file is a part of the implementation for the paper: +% Lalit Manam and Venu Madhav Govindu, Correspondence Reweighted +% Translation Averaging, Proceedings of the European Conference on +% Computer Vision 2022 + +% This script is modified from the source: +% https://bbzh.github.io/document/BATA.zip + +function [t,ed_ret_idx]=BATA(tij_index,tij_observe,param,t_init_given,idxConst) +% Inputs:: +% tij_index: 2 by n matrix specifying the edge (i,j) +% tij_observe: 3 by n matrix specifying tij for each edge (i,j), such that +% tj-ti/norm(tj-ti) = tij +% param: Parameters for the method +% t_init_given: 3 by m matrix of camera translation initialization +% idxConst: Camera index which is chosen as origin +% Outputs:: +% t: 3 by m matrix specifying the camera translations +% ed_ret_idx: indexes of the edge retained + +if(nargin<4) + t_init_given=[]; +end + + numofcam = max(max(tij_index)); + numofobser = size(tij_observe,2); + Ri_T = repmat(eye(3),numofobser,1); + Rj_T=Ri_T; + + index_ti_I = [(1:3*numofobser)' (1:3*numofobser)' (1:3*numofobser)']; % position of coefficient for Ri + index_ti_J = (tij_index(1,:)-1)*3+1; + index_ti_J = [index_ti_J index_ti_J+1 index_ti_J+2]; + index_ti_J = index_ti_J(ceil((1:3*size(index_ti_J,1))/3), :); + index_tj_I = [(1:3*numofobser)' (1:3*numofobser)' (1:3*numofobser)']; % position of coefficient for Rj + index_tj_J = (tij_index(2,:)-1)*3+1; + index_tj_J = [index_tj_J index_tj_J+1 index_tj_J+2]; + index_tj_J = index_tj_J(ceil((1:3*size(index_tj_J,1))/3), :); + + At0_full = sparse(index_ti_I,index_ti_J, Ri_T,3*numofobser,3*numofcam)... + -sparse(index_tj_I,index_tj_J,Rj_T,3*numofobser,3*numofcam); + + Z=zeros(4); + if(isempty(t_init_given)) + Aeq1 = sparse(reshape(repmat(1:numofobser,3,1),[],1),1:3*numofobser,tij_observe)*At0_full; + Aeq1 = sum(Aeq1); + beq1 = numofobser; + Aeq2 = repmat(eye(3),1,numofcam); + beq2 = zeros(3,1); + Aeq = [Aeq1;Aeq2]; + beq = [beq1;beq2]; + + % Initialization with LUDRevised + Svec = rand(1,numofobser)+0.5; + Svec = Svec/sum(Svec)*numofobser; + S = reshape(repmat(Svec,3,1),[],1); + W = ones(3*numofobser,1); + ii = 1; + + errPrev=1; errCurr=0; + while(ii<=param.numofiterinit && abs(errPrev-errCurr)/errPrev>1e-5) + A = sparse(1:3*numofobser,1:3*numofobser,W)*At0_full; + B = W.*S.*tij_observe(:); + X = [(A'*A) Aeq'; Aeq Z]\[(A'*B); beq]; + t = X(1:3*numofcam); + Aij = reshape(At0_full*t,3,numofobser); + tij_T = reshape(tij_observe,3,numofobser); + Svec = sum(Aij.*tij_T)./sum(tij_T.*tij_T); + Svec(Svec<0)=0; + tmp3 = repmat(Svec,[3,1]); + S = tmp3(:); + errPrev=errCurr; + tmp= sum(reshape(At0_full*t-S.*tij_observe(:),3,[]).^2); + Wvec = (tmp + param.delta).^(-0.5); + errCurr=sum(sqrt(tmp)); + W = reshape(repmat(sqrt(Wvec),[3,1]),[],1); + ii = ii + 1; + end + else + t=t_init_given(:); + end + + % RLUD init + ii = 1; + node_ret_idx=true(3,numofcam); + errPrev=1; errCurr=0; + beq2 = zeros(3,1); + I=eye(3); + while(ii<=param.numofouteriter && (abs(errPrev-errCurr)/errPrev)>1e-5) + + Aij = reshape(At0_full*t,3,numofobser); + tij_T = reshape(tij_observe,3,numofobser); + Svec = sum(Aij.*tij_T)./sum(tij_T.*tij_T); + + [ed_ret_idx,node_ret_idx]=extractLargestConnComp(Svec,tij_index,node_ret_idx); + numEdgesRet=sum(ed_ret_idx(1,:)); + numNodesRet=sum(node_ret_idx(1,:)); + + t2=t(node_ret_idx(:)); + Svec=Svec(ed_ret_idx(1,:)); + At0_fullr=At0_full(ed_ret_idx(:),node_ret_idx(:)); + + tij_T=tij_observe(:,ed_ret_idx(1,:)); + tmp3 = repmat(Svec,[3,1]); + S = tmp3(:); + tmp = reshape(At0_fullr*t2-S.*tij_T(:),3,[]); + errPrev=errCurr; + errCurr=sum(vecnorm(tmp,2,1)); + + Wvec = (sum(tmp.*tmp) + param.delta).^(-0.5); + W = reshape(repmat(sqrt(Wvec),[3,1]),[],1); + + A = sparse(1:3*numEdgesRet,1:3*numEdgesRet,W)*At0_fullr; + B = W.*S.*tij_T(:); + Aeq1 = sparse(reshape(repmat(1:numEdgesRet,3,1),[],1),1:3*numEdgesRet,tij_T)*At0_fullr; + Aeq1 = sum(Aeq1); + beq1 = numEdgesRet; + Aeq2 = repmat(I,1,numNodesRet); + X = [(A'*A) Aeq1' Aeq2'; [[Aeq1; Aeq2], Z]]\[(A'*B); beq1; beq2]; + t(node_ret_idx(:))=X(1:3*numNodesRet); + ii = ii + 1; + end + + % BATA + t=t-repmat(t(3*idxConst-2:3*idxConst),numofcam,1); + node_ret_idx_temp=true(3,numofcam); node_ret_idx_temp(:,idxConst)=false; + t=t(node_ret_idx_temp(:)); + At0 = At0_full(:,node_ret_idx_temp(:)); + errPrev=1; errCurr=0; ii=1; + while(ii<=param.numofouteriter && (abs(errCurr-errPrev)/errPrev)>1e-5) + + Aij = reshape(At0*t,3,numofobser); + tij_T_weighted = reshape(tij_observe,3,numofobser); + Svec = sum(Aij.*Aij)./sum(Aij.*tij_T_weighted); + tmp3=repmat(Svec,[3,1]); S=tmp3(:); + + A = sparse(1:length(S),1:length(S),1./S,length(S),length(S))*At0; + B = tij_observe(:); + tmp = sqrt(sum(reshape((A*t-B).^2,3,[]))); + errPrev=errCurr; + errCurr=sum(tmp(ed_ret_idx(1,:))); + Wvec=1./(1+((tmp/param.robustthre).^2)); + jj =1; + while(jj<=param.numofinneriter) + + Aij = reshape(At0*t,3,numofobser); + tij_T_weighted = reshape(tij_observe,3,numofobser); + Svec = sum(Aij.*Aij)./sum(Aij.*tij_T_weighted); + + [ed_ret_idx,node_ret_idx]=extractLargestConnComp(Svec,tij_index,node_ret_idx); + node_ret_idx_temp=node_ret_idx; node_ret_idx_temp(:,idxConst)=false; + Svec=Svec(ed_ret_idx(1,:)); + tmp3 = repmat(Svec,[3,1]); + S_red = tmp3(:); + Wvec2=Wvec(ed_ret_idx(1,:)); + At0_fullr=At0_full(ed_ret_idx(:),node_ret_idx_temp(:)); + tij_T=tij_observe(:,ed_ret_idx(1,:)); + W = reshape(repmat(sqrt(Wvec2),3,1),[],1); + + A = sparse(1:length(S_red),1:length(S_red),W./S_red,length(S_red),length(S_red))*At0_fullr; + B = W.*tij_T(:); + t2=(A'*A)\(A'*B); + node_ret_idx_temp2=node_ret_idx_temp(:,setdiff(1:end,idxConst)); + t(node_ret_idx_temp2(:))=t2; + jj = jj +1; + end + ii = ii + 1; + end + t=reshape(t,3,[]); + t=[t(:,1:idxConst-1),[0;0;0],t(:,idxConst:end)]; + t(~node_ret_idx(:))=NaN; + t=t-mean(t,2,'omitnan'); + nodes=find(node_ret_idx(1,:)); + ed_ret_idx=ismember(tij_index(1,:),nodes)&ismember(tij_index(2,:),nodes); +end + +function [ed_ret_idx,node_ret_idx]=extractLargestConnComp(Svec,tij_index,node_ret_idx) + ed_ret_idx=true(3,size(tij_index,2)); + ed_ret_idx(:,Svec<0)=false; + nodes=find(node_ret_idx(1,:)); + eidx=ismember(tij_index(1,:),nodes)&ismember(tij_index(2,:),nodes); + ed_ret_idx(:,~eidx)=false; + edges=tij_index(:,ed_ret_idx(1,:)); + + G=graph(edges(1,:),edges(2,:)); + bins=conncomp(G,'OutputForm','vector'); + nodes = find(bins==mode(bins)); + ed_ret_idx_temp=ismember(tij_index(1,:),nodes)&ismember(tij_index(2,:),nodes)&ed_ret_idx(1,:); + ed_ret_idx=repmat(ed_ret_idx_temp,[3,1]); + node_ret_idx(:)=false; + node_ret_idx(:,nodes)=true; +end diff --git a/thirdparty/creta/BATACost.m b/thirdparty/creta/BATACost.m new file mode 100644 index 000000000..de789baf5 --- /dev/null +++ b/thirdparty/creta/BATACost.m @@ -0,0 +1,49 @@ +% Copyright (C) 2022 Computer Vision Lab, Electrical Engineering, +% Indian Institute of Science, Bengaluru, India. +% All rights reserved. +% +% Redistribution and use in source and binary forms, with or without +% modification, are permitted provided that the following conditions are +% met: +% +% * Redistributions of source code must retain the above copyright +% notice, this list of conditions and the following disclaimer. +% * Redistributions in binary form must reproduce the above +% copyright notice, this list of conditions and the following +% disclaimer in the documentation and/or other materials provided +% with the distribution. +% * Neither the name of Indian Institute of Science nor the +% names of its contributors may be used to endorse or promote products +% derived from this software without specific prior written permission. +% +% THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +% OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +% FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL +% THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +% LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +% FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER +% DEALINGS IN THE SOFTWARE. +% +% Author: Lalit Manam +% +% This file is a part of the implementation for the paper: +% Lalit Manam and Venu Madhav Govindu, Correspondence Reweighted +% Translation Averaging, Proceedings of the European Conference on +% Computer Vision 2022 + +function [cost] = BATACost(RT,edges,T) +% This function determines BATA cost for a given solution T +% Inputs: +% RT: 3XN matrix of (Cj-Ci)/norm(Cj-Ci) +% edges: MX2 matrix of edge +% T: 3XN matrix of camera locations +% Outputs: +% cost: Mean error of all the edge costs + + T_Diff=T(:,edges(:,2))-T(:,edges(:,1)); + T_DiffNorm=sum(T_Diff.*RT)./sum(T_Diff.*T_Diff); + T_DiffNorm(T_DiffNorm<0)=0; + costall=vecnorm(T_Diff.*T_DiffNorm-RT,2,1); + costall(isnan(costall))=[]; + cost=mean(costall,2); +end \ No newline at end of file diff --git a/thirdparty/creta/CReTA_demo.asv b/thirdparty/creta/CReTA_demo.asv new file mode 100644 index 000000000..7346980ce --- /dev/null +++ b/thirdparty/creta/CReTA_demo.asv @@ -0,0 +1,196 @@ +% Copyright (C) 2022 Computer Vision Lab, Electrical Engineering, +% Indian Institute of Science, Bengaluru, India. +% All rights reserved. +% +% Redistribution and use in source and binary forms, with or without +% modification, are permitted provided that the following conditions are +% met: +% * Redistributions of source code must retain the above copyright +% notice, this list of conditions and the following disclaimer. +% * Redistributions in binary form must reproduce the above +% copyright notice, this list of conditions and the following +% disclaimer in the documentation and/or other materials provided +% with the distribution. +% * Neither the name of Indian Institute of Science nor the +% names of its contributors may be used to endorse or promote products +% derived from this software without specific prior written permission. +% +% THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +% OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +% FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL +% THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +% LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +% FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER +% DEALINGS IN THE SOFTWARE. +% +% Author: Lalit Manam +% +% This file is a part of the implementation for the paper: +% Lalit Manam and Venu Madhav Govindu, Correspondence Reweighted +% Translation Averaging, Proceedings of the European Conference on +% Computer Vision 2022 + +% Demo for CReTA framework +clear; close all +% parpool; + +extractParallelRigidSubgraph=true; +extractParallelRigidSubgraphIter=false; +loadGTComponent=false; +loadRavgFilteredGraph=true; + +computeInitialRelTransIRLS=false; +computeInitialRelTransRANSAC=true; + +stepNormConvTol=1e-6; +TACostRelConvTol=1e-5; + +TAMethod='RLUD' + +% Translation Averaging Parameters +if(strcmp(TAMethod,'RLUD')) + maxIters=5000; + % RLUD Parameters + TAparams.delta = 10^-5; + TAparams.numofiterinit = 50; + TAparams.numofouteriter = 20; + TAparams.robustthre = 10^-1; +elseif(strcmp(TAMethod,'BATA')) + maxIters=500; + % Parameters for RLUD-BATA + TAparams.delta = 10^-5; + TAparams.numofiterinit = 50; + TAparams.numofouteriter = 20; + TAparams.numofinneriter = 5; + TAparams.robustthre = 10^-1; +else + error('Invalid TA method'); +end + +% Relative translation estimation parameters +RTparams.SIGMA=1e-2; + +% Filter Edges in Iteration +FEparams.maxAngleDeg=5; + +%% Load data +%load('dummy_data.mat'); +load("/home/tdriver6/Documents/gtsfm/ta_input_6bce599e687072b0c2a6421e66c30eb6fee27c8e.mat"); +matches = reshape(matches, [length(matches) 1]); +edges = edges + 1; +maxImages = max(edges(:)); +% The data should contain the following variables: +% Graph has N nodes and M edges. +% RT: 3XM matrix of relative translation directions (Tij=Rj*(Ti-Tj)). +% edges: Mx2 matrix of camera pairs.RT +% matches: Mx1 cell each containing Kx4 matrix. +% Each matrix contains the point correspondences for each edge. +% Each row in the matrix contains (xi,yi,xj,yj) where xi,yi and xj,yj +% are coordinates for images i,j respectively afer correcting for camera intrinsics. +% R_avg: 3X3XN matrix of absolute rotations. +% maxImages: Maimum No. of images in the dataset (used for indexing) +% NOTE: Ensure that the graph is connected and has the maximal parallel +% rigid component. + +%% Compute unit norm feature vectors +for i=1:size(matches,1) + tempMat=matches{i}; + numInliers=size(tempMat,1); + x1=[tempMat(:,1:2),ones(numInliers,1)]; + x1=x1./vecnorm(x1,2,2); + x2=[tempMat(:,3:4),ones(numInliers,1)]; + x2=x2./vecnorm(x2,2,2); + matches{i}=[x1,x2]; +end + +%% Pre processing matches +for i=1:size(matches,1) + tempMat=matches{i}; + tempMat(:,1:3)=tempMat(:,1:3)*R_avg(:,:,edges(i,1)); + tempMat(:,4:6)=tempMat(:,4:6)*R_avg(:,:,edges(i,2)); + matches{i}=cross(tempMat(:,4:6),tempMat(:,1:3)); +end + +%% Pre processing relative translations +for i=1:size(edges,1) + RT(:,i)=-R_avg(:,:,edges(i,2))'*RT(:,i); +end + +%% CReTA +iter=1; stepNorm=1; tacostprev=100; tacost=1; +T_avg=[]; + +while(iter<=maxIters && stepNorm>stepNormConvTol && abs(tacostprev-tacost)/tacostprev>TACostRelConvTol) + % Run Translation Averaging + C_avg_prev=T_avg; + tacostprev=tacost; + [T_avg,tacost,ed_ret_idx] = RunTA(edges,RT,T_avg,maxImages,TAparams,TAMethod); + + % Remove edges that were filtered out + eidx=~ed_ret_idx; + edges(eidx,:)=[]; + RT(:,eidx)=[]; + matches(eidx)=[]; + + % Filter Edges in iteration + RT_camSol=T_avg(:,edges(:,2))-T_avg(:,edges(:,1)); + RT_camSol=RT_camSol./vecnorm(RT_camSol,2,1); + dotProduct=sum(RT.*RT_camSol,1); + angles = abs(acos(dotProduct)); + SolCalcDiffAngles = angles*180/pi; + + eidx=SolCalcDiffAngles>FEparams.maxAngleDeg; + edges(eidx,:)=[]; + RT(:,eidx)=[]; + matches(eidx)=[]; + + % Extract largest connected component + G=graph(edges(:,1),edges(:,2)); + bins = conncomp(G,'OutputForm','vector'); + nodes = find(bins==mode(bins)); % Map from new (idx) to old (value) + + eidxRet=ismember(edges(:,1),nodes)&ismember(edges(:,2),nodes); + eidx=~eidxRet; + edges(eidx,:)=[]; + RT(:,eidx)=[]; + matches(eidx)=[]; + + % Compute relative translations + for i=1:size(edges,1) + tij=(T_avg(:,edges(i,2))-T_avg(:,edges(i,1))); + tij=tij/norm(tij); + tempMat=matches{i}; + err_vec=abs(tempMat*tij); + % Remove points at 1st iteration + if(iter==1) + s_err=sort(err_vec); + remPtsIdx=err_vec>s_err(ceil(numel(s_err)*0.75)); + err_vec(remPtsIdx)=[]; + tempMat(remPtsIdx,:)=[]; + matches{i}=tempMat; + end + w_vec=1./(1+(err_vec/RTparams.SIGMA).^2); + [~,s,v]=svd(tempMat'*(w_vec.*tempMat)); + tij_ref=v(:,end); + if(tij'*tij_ref<0) + tij_ref=-tij_ref; + end + RT(:,i)=tij_ref; + end + + % Update parameters + if(iter>1) + stepDiff=C_avg_prev-T_avg; + stepDiff(:,any(isnan(stepDiff),1))=[]; + stepNorm=mean(vecnorm(stepDiff,2,1)); + end + disp(['Iteration: ',num2str(iter),' done']); + iter=iter+1; +end + +disp('Completed!'); + +save('/home/tdriver6/Documents/gtsfm/ta__6bce599e687072b0c2a6421e66c30eb6fee27c8e.mat', 'T_avg', 'edges') + +plot3(T_avg(1,:),T_avg(2,:),T_avg(3,:), "."); +axis equal; diff --git a/thirdparty/creta/CReTA_demo.m b/thirdparty/creta/CReTA_demo.m new file mode 100644 index 000000000..2b4b4fb43 --- /dev/null +++ b/thirdparty/creta/CReTA_demo.m @@ -0,0 +1,197 @@ +% Copyright (C) 2022 Computer Vision Lab, Electrical Engineering, +% Indian Institute of Science, Bengaluru, India. +% All rights reserved. +% +% Redistribution and use in source and binary forms, with or without +% modification, are permitted provided that the following conditions are +% met: +% * Redistributions of source code must retain the above copyright +% notice, this list of conditions and the following disclaimer. +% * Redistributions in binary form must reproduce the above +% copyright notice, this list of conditions and the following +% disclaimer in the documentation and/or other materials provided +% with the distribution. +% * Neither the name of Indian Institute of Science nor the +% names of its contributors may be used to endorse or promote products +% derived from this software without specific prior written permission. +% +% THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +% OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +% FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL +% THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +% LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +% FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER +% DEALINGS IN THE SOFTWARE. +% +% Author: Lalit Manam +% +% This file is a part of the implementation for the paper: +% Lalit Manam and Venu Madhav Govindu, Correspondence Reweighted +% Translation Averaging, Proceedings of the European Conference on +% Computer Vision 2022 + +% Demo for CReTA framework +clear; close all +% parpool; + +extractParallelRigidSubgraph=true; +extractParallelRigidSubgraphIter=false; +loadGTComponent=false; +loadRavgFilteredGraph=true; + +computeInitialRelTransIRLS=false; +computeInitialRelTransRANSAC=true; + +stepNormConvTol=1e-6; +TACostRelConvTol=1e-5; + +TAMethod='RLUD' + +% Translation Averaging Parameters +if(strcmp(TAMethod,'RLUD')) + maxIters=5000; + % RLUD Parameters + TAparams.delta = 10^-5; + TAparams.numofiterinit = 50; + TAparams.numofouteriter = 20; + TAparams.robustthre = 10^-1; +elseif(strcmp(TAMethod,'BATA')) + maxIters=500; + % Parameters for RLUD-BATA + TAparams.delta = 10^-5; + TAparams.numofiterinit = 50; + TAparams.numofouteriter = 20; + TAparams.numofinneriter = 5; + TAparams.robustthre = 10^-1; +else + error('Invalid TA method'); +end + +% Relative translation estimation parameters +RTparams.SIGMA=1e-2; + +% Filter Edges in Iteration +FEparams.maxAngleDeg=5; + +%% Load data +%load('dummy_data.mat'); +matches_hash = "6bce599e687072b0c2a6421e66c30eb6fee27c8e"; +load("/home/tdriver6/Documents/gtsfm/ta_input_" + matches_hash + ".mat"); +matches = reshape(matches, [length(matches) 1]); +edges = edges + 1; +maxImages = max(edges(:)); +% The data should contain the following variables: +% Graph has N nodes and M edges. +% RT: 3XM matrix of relative translation directions (Tij=Rj*(Ti-Tj)). +% edges: Mx2 matrix of camera pairs.RT +% matches: Mx1 cell each containing Kx4 matrix. +% Each matrix contains the point correspondences for each edge. +% Each row in the matrix contains (xi,yi,xj,yj) where xi,yi and xj,yj +% are coordinates for images i,j respectively afer correcting for camera intrinsics. +% R_avg: 3X3XN matrix of absolute rotations. +% maxImages: Maimum No. of images in the dataset (used for indexing) +% NOTE: Ensure that the graph is connected and has the maximal parallel +% rigid component. + +%% Compute unit norm feature vectors +for i=1:size(matches,1) + tempMat=matches{i}; + numInliers=size(tempMat,1); + x1=[tempMat(:,1:2),ones(numInliers,1)]; + x1=x1./vecnorm(x1,2,2); + x2=[tempMat(:,3:4),ones(numInliers,1)]; + x2=x2./vecnorm(x2,2,2); + matches{i}=[x1,x2]; +end + +%% Pre processing matches +for i=1:size(matches,1) + tempMat=matches{i}; + tempMat(:,1:3)=tempMat(:,1:3)*R_avg(:,:,edges(i,1)); + tempMat(:,4:6)=tempMat(:,4:6)*R_avg(:,:,edges(i,2)); + matches{i}=cross(tempMat(:,4:6),tempMat(:,1:3)); +end + +%% Pre processing relative translations +for i=1:size(edges,1) + RT(:,i)=-R_avg(:,:,edges(i,2))'*RT(:,i); +end + +%% CReTA +iter=1; stepNorm=1; tacostprev=100; tacost=1; +T_avg=[]; + +while(iter<=maxIters && stepNorm>stepNormConvTol && abs(tacostprev-tacost)/tacostprev>TACostRelConvTol) + % Run Translation Averaging + C_avg_prev=T_avg; + tacostprev=tacost; + [T_avg,tacost,ed_ret_idx] = RunTA(edges,RT,T_avg,maxImages,TAparams,TAMethod); + + % Remove edges that were filtered out + eidx=~ed_ret_idx; + edges(eidx,:)=[]; + RT(:,eidx)=[]; + matches(eidx)=[]; + + % Filter Edges in iteration + RT_camSol=T_avg(:,edges(:,2))-T_avg(:,edges(:,1)); + RT_camSol=RT_camSol./vecnorm(RT_camSol,2,1); + dotProduct=sum(RT.*RT_camSol,1); + angles = abs(acos(dotProduct)); + SolCalcDiffAngles = angles*180/pi; + + eidx=SolCalcDiffAngles>FEparams.maxAngleDeg; + edges(eidx,:)=[]; + RT(:,eidx)=[]; + matches(eidx)=[]; + + % Extract largest connected component + G=graph(edges(:,1),edges(:,2)); + bins = conncomp(G,'OutputForm','vector'); + nodes = find(bins==mode(bins)); % Map from new (idx) to old (value) + + eidxRet=ismember(edges(:,1),nodes)&ismember(edges(:,2),nodes); + eidx=~eidxRet; + edges(eidx,:)=[]; + RT(:,eidx)=[]; + matches(eidx)=[]; + + % Compute relative translations + for i=1:size(edges,1) + tij=(T_avg(:,edges(i,2))-T_avg(:,edges(i,1))); + tij=tij/norm(tij); + tempMat=matches{i}; + err_vec=abs(tempMat*tij); + % Remove points at 1st iteration + if(iter==1) + s_err=sort(err_vec); + remPtsIdx=err_vec>s_err(ceil(numel(s_err)*0.75)); + err_vec(remPtsIdx)=[]; + tempMat(remPtsIdx,:)=[]; + matches{i}=tempMat; + end + w_vec=1./(1+(err_vec/RTparams.SIGMA).^2); + [~,s,v]=svd(tempMat'*(w_vec.*tempMat)); + tij_ref=v(:,end); + if(tij'*tij_ref<0) + tij_ref=-tij_ref; + end + RT(:,i)=tij_ref; + end + + % Update parameters + if(iter>1) + stepDiff=C_avg_prev-T_avg; + stepDiff(:,any(isnan(stepDiff),1))=[]; + stepNorm=mean(vecnorm(stepDiff,2,1)); + end + disp(['Iteration: ',num2str(iter),' done']); + iter=iter+1; +end + +disp('Completed!'); + +save("/home/tdriver6/Documents/gtsfm/ta_output_" + matches_hash + ".mat", 'T_avg', 'edges') + +plot3(T_avg(1,:),T_avg(2,:),T_avg(3,:), "."); +axis equal; diff --git a/thirdparty/creta/LUDCost.m b/thirdparty/creta/LUDCost.m new file mode 100644 index 000000000..9944a30dd --- /dev/null +++ b/thirdparty/creta/LUDCost.m @@ -0,0 +1,49 @@ +% Copyright (C) 2022 Computer Vision Lab, Electrical Engineering, +% Indian Institute of Science, Bengaluru, India. +% All rights reserved. +% +% Redistribution and use in source and binary forms, with or without +% modification, are permitted provided that the following conditions are +% met: +% +% * Redistributions of source code must retain the above copyright +% notice, this list of conditions and the following disclaimer. +% * Redistributions in binary form must reproduce the above +% copyright notice, this list of conditions and the following +% disclaimer in the documentation and/or other materials provided +% with the distribution. +% * Neither the name of Indian Institute of Science nor the +% names of its contributors may be used to endorse or promote products +% derived from this software without specific prior written permission. +% +% THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +% OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +% FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL +% THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +% LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +% FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER +% DEALINGS IN THE SOFTWARE. +% +% Author: Lalit Manam +% +% This file is a part of the implementation for the paper: +% Lalit Manam and Venu Madhav Govindu, Correspondence Reweighted +% Translation Averaging, Proceedings of the European Conference on +% Computer Vision 2022 + +function [cost] = LUDCost(RT,edges,T) +% This function determines LUD cost for a given T +% Inputs: +% RT: 3XN matrix of (Cj-Ci)/norm(Cj-Ci) +% edges: MX2 matrix of edge +% T: 3XN matrix of camera locations +% Outputs: +% cost: Mean error of all the edge costs + + T_Diff=T(:,edges(:,2))-T(:,edges(:,1)); + T_DiffNorm=sum(T_Diff.*RT); + T_DiffNorm(T_DiffNorm<0)=0; + costall=vecnorm(T_Diff-T_DiffNorm.*RT,2,1); + costall(isnan(costall))=[]; + cost=mean(costall,2); +end \ No newline at end of file diff --git a/thirdparty/creta/RLUD.m b/thirdparty/creta/RLUD.m new file mode 100644 index 000000000..c5ff31a4a --- /dev/null +++ b/thirdparty/creta/RLUD.m @@ -0,0 +1,173 @@ +% Copyright (C) 2022 Computer Vision Lab, Electrical Engineering, +% Indian Institute of Science, Bengaluru, India. +% All rights reserved. +% +% Redistribution and use in source and binary forms, with or without +% modification, are permitted provided that the following conditions are +% met: +% +% * Redistributions of source code must retain the above copyright +% notice, this list of conditions and the following disclaimer. +% * Redistributions in binary form must reproduce the above +% copyright notice, this list of conditions and the following +% disclaimer in the documentation and/or other materials provided +% with the distribution. +% * Neither the name of Indian Institute of Science nor the +% names of its contributors may be used to endorse or promote products +% derived from this software without specific prior written permission. +% +% THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +% OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +% FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL +% THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +% LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +% FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER +% DEALINGS IN THE SOFTWARE. +% +% Author: Lalit Manam +% +% This file is a part of the implementation for the paper: +% Lalit Manam and Venu Madhav Govindu, Correspondence Reweighted +% Translation Averaging, Proceedings of the European Conference on +% Computer Vision 2022 + +% This script is modified from the source: +% https://bbzh.github.io/document/BATA.zip + +function [t,ed_ret_idx]=RLUD(tij_index,tij_observe,param,t_init_given) +% Inputs:: +% tij_index: 2 by n matrix specifying the edge (i,j) +% tij_observe: 3 by n matrix specifying tij for each edge (i,j), such that +% tj-ti/norm(tj-ti) = tij +% param: Parameters for the method +% t_init_given: 3 by m matrix of camera translation initialization +% Outputs:: +% t: 3 by m matrix specifying the camera translations +% ed_ret_idx: indexes of the edge retained + +if(nargin<4) + t_init_given=[]; +end + + numofcam = max(max(tij_index)); + numofobser = size(tij_observe,2); + Ri_T = repmat(eye(3),numofobser,1); + Rj_T=Ri_T; + + index_ti_I = [(1:3*numofobser)' (1:3*numofobser)' (1:3*numofobser)']; % position of coefficient for Ri + index_ti_J = (tij_index(1,:)-1)*3+1; + index_ti_J = [index_ti_J index_ti_J+1 index_ti_J+2]; + index_ti_J = index_ti_J(ceil((1:3*size(index_ti_J,1))/3), :); + index_tj_I = [(1:3*numofobser)' (1:3*numofobser)' (1:3*numofobser)']; % position of coefficient for Rj + index_tj_J = (tij_index(2,:)-1)*3+1; + index_tj_J = [index_tj_J index_tj_J+1 index_tj_J+2]; + index_tj_J = index_tj_J(ceil((1:3*size(index_tj_J,1))/3), :); + + At0_full = sparse(index_ti_I,index_ti_J, Ri_T,3*numofobser,3*numofcam)... + -sparse(index_tj_I,index_tj_J,Rj_T,3*numofobser,3*numofcam); + + Z=zeros(4); + if(isempty(t_init_given)) + Aeq1 = sparse(reshape(repmat(1:numofobser,3,1),[],1),1:3*numofobser,tij_observe)*At0_full; + Aeq1 = sum(Aeq1); + beq1 = numofobser; + Aeq2 = repmat(eye(3),1,numofcam); + beq2 = zeros(3,1); + Aeq = [Aeq1;Aeq2]; + beq = [beq1;beq2]; + + % Initialization with LUDRevised + Svec = rand(1,numofobser)+0.5; + Svec = Svec/sum(Svec)*numofobser; + S = reshape(repmat(Svec,3,1),[],1); + W = ones(3*numofobser,1); + ii = 1; + tmp=zeros(3,numofobser); tmpPrev=zeros(size(tmp))+Inf; + + while(ii<=param.numofiterinit && sum(vecnorm(tmpPrev-tmp))/sum(vecnorm(tmp))>1e-5) + A = sparse(1:3*numofobser,1:3*numofobser,W)*At0_full; + B = W.*S.*tij_observe(:); + X = [(A'*A) Aeq'; Aeq Z]\[(A'*B); beq]; + t = X(1:3*numofcam); + Aij = reshape(At0_full*t,3,numofobser); + tij_T = reshape(tij_observe,3,numofobser); + Svec = sum(Aij.*tij_T)./sum(tij_T.*tij_T); + Svec(Svec<0)=0; + tmp3 = repmat(Svec,[3,1]); + S = tmp3(:); + tmpPrev=tmp; + tmp = reshape(At0_full*t-S.*tij_observe(:),3,[]); + Wvec = (sum(tmp.*tmp) + param.delta).^(-0.5); + W = reshape(repmat(sqrt(Wvec),[3,1]),[],1); + ii = ii + 1; + end + else + t=t_init_given(:); + end + + % RLUD + ii = 1; + node_ret_idx=true(3,numofcam); + tprev=inf(size(t)); + errPrevRLUD=1; errCurrRLUD=0; + beq2 = zeros(3,1); + I=eye(3); + while(ii<=param.numofouteriter && norm(tprev(node_ret_idx(:))-t(node_ret_idx(:)))>1e-3 && ... + (abs(errPrevRLUD-errCurrRLUD)/errPrevRLUD)>1e-5) + tprev=t; + + Aij = reshape(At0_full*t,3,numofobser); + tij_T = reshape(tij_observe,3,numofobser); + Svec = sum(Aij.*tij_T)./sum(tij_T.*tij_T); + + [ed_ret_idx,node_ret_idx]=extractLargestConnComp(Svec,tij_index,node_ret_idx); + numEdgesRet=sum(ed_ret_idx(1,:)); + numNodesRet=sum(node_ret_idx(1,:)); + + t2=t(node_ret_idx(:)); + Svec=Svec(ed_ret_idx(1,:)); + At0_fullr=At0_full(ed_ret_idx(:),node_ret_idx(:)); + + tij_T=tij_observe(:,ed_ret_idx(1,:)); + tmp3 = repmat(Svec,[3,1]); + S = tmp3(:); + tmp = reshape(At0_fullr*t2-S.*tij_T(:),3,[]); + errPrevRLUD=errCurrRLUD; + errCurrRLUD=sum(vecnorm(tmp,2,1)); + + Wvec = (sum(tmp.*tmp) + param.delta).^(-0.5); + W = reshape(repmat(sqrt(Wvec),[3,1]),[],1); + + A = sparse(1:3*numEdgesRet,1:3*numEdgesRet,W)*At0_fullr; + B = W.*S.*tij_T(:); + Aeq1 = sparse(reshape(repmat(1:numEdgesRet,3,1),[],1),1:3*numEdgesRet,tij_T)*At0_fullr; + Aeq1 = sum(Aeq1); + beq1 = numEdgesRet; + Aeq2 = repmat(I,1,numNodesRet); + X = [(A'*A) Aeq1' Aeq2'; [[Aeq1; Aeq2], Z]]\[(A'*B); beq1; beq2]; + t(node_ret_idx(:))=X(1:3*numNodesRet); + ii = ii + 1; + end + + t(~node_ret_idx(:))=NaN; + t=reshape(t,3,[]); + nodes=find(node_ret_idx(1,:)); + ed_ret_idx=ismember(tij_index(1,:),nodes)&ismember(tij_index(2,:),nodes); +end + +function [ed_ret_idx,node_ret_idx]=extractLargestConnComp(Svec,tij_index,node_ret_idx) + ed_ret_idx=true(3,size(tij_index,2)); + ed_ret_idx(:,Svec<0)=false; + nodes=find(node_ret_idx(1,:)); + eidx=ismember(tij_index(1,:),nodes)&ismember(tij_index(2,:),nodes); + ed_ret_idx(:,~eidx)=false; + edges=tij_index(:,ed_ret_idx(1,:)); + + G=graph(edges(1,:),edges(2,:)); + bins=conncomp(G,'OutputForm','vector'); + nodes=find(bins==mode(bins)); + ed_ret_idx_temp=ismember(tij_index(1,:),nodes)&ismember(tij_index(2,:),nodes)&ed_ret_idx(1,:); + ed_ret_idx=repmat(ed_ret_idx_temp,[3,1]); + node_ret_idx(:)=false; + node_ret_idx(:,nodes)=true; +end diff --git a/thirdparty/creta/ReadMe.txt b/thirdparty/creta/ReadMe.txt new file mode 100644 index 000000000..b81f13957 --- /dev/null +++ b/thirdparty/creta/ReadMe.txt @@ -0,0 +1,17 @@ +Copyright (C) 2022 Computer Vision Lab, Electrical Engineering, +Indian Institute of Science, Bengaluru, India. +All rights reserved. + +This file is a part of the implementation for the paper: +Lalit Manam and Venu Madhav Govindu, Correspondence Reweighted Translation Averaging, Proceedings of the European Conference on Computer Vision 2022. + +Code tested on MATLAB R2020b. + +MATLAB toolbox required: +1. Parallel Computing Toolbox (optional) + +NOTE: +If the toolbox is available, uncomment line 35 and replace "for" with "parfor" in line 155 of the script CReTA_demo.m. + +Codes for RLUD and BATA are provided at: +https://bbzh.github.io/document/BATA.zip diff --git a/thirdparty/creta/RunTA.asv b/thirdparty/creta/RunTA.asv new file mode 100644 index 000000000..7ba2b5eb3 --- /dev/null +++ b/thirdparty/creta/RunTA.asv @@ -0,0 +1,83 @@ +% Copyright (C) 2022 Computer Vision Lab, Electrical Engineering, +% Indian Institute of Science, Bengaluru, India. +% All rights reserved. +% +% Redistribution and use in source and binary forms, with or without +% modification, are permitted provided that the following conditions are +% met: +% +% * Redistributions of source code must retain the above copyright +% notice, this list of conditions and the following disclaimer. +% * Redistributions in binary form must reproduce the above +% copyright notice, this list of conditions and the following +% disclaimer in the documentation and/or other materials provided +% with the distribution. +% * Neither the name of Indian Institute of Science nor the +% names of its contributors may be used to endorse or promote products +% derived from this software without specific prior written permission. +% +% THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +% OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +% FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL +% THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +% LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +% FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER +% DEALINGS IN THE SOFTWARE. +% +% Author: Lalit Manam +% +% This file is a part of the implementation for the paper: +% Lalit Manam and Venu Madhav Govindu, Correspondence Reweighted +% Translation Averaging, Proceedings of the European Conference on +% Computer Vision 2022 + +function [T_avg,tacost,ed_ret_idx] = RunTA(edges,RT,T_avg,maxImages,param,TAMethod) +% Run Translation Averaging (TA) +% Inputs:: +% edges: MX2 matrix of camera pairs +% RT: 3XM matrix of relative translations +% T_avg: 3XN matrix of camera locations +% maxImages: Maximum no of images for indexing +% param: Parameters for TA Method +% TAMethod: 'RLUD' or 'BATA' +% Outputs: +% T_avg: 3XN matrix of camera locations +% tacost: Valur of TA cost at the solution +% ed_ret_idx: Edge indexes used for translation averaging + +% Relabelling nodes +nodes=unique(edges(:)); +G=graph(edges(:,1),edges(:,2),1:size(edges,1)); +D=degree(G); [~,loc]=max(D); +idxConst=find(nodes==loc); +G1=subgraph(G,nodes); +P=G1.Edges; +edges_sg=P.EndNodes; +idx=find(edges(:,1)>edges(:,2)); +edge_idx_map=P.Weight; +RT(:,idx)=-RT(:,idx); +RT_sg=RT(:,edge_idx_map); + +if(isempty(T_avg)) + C_avg_temp=[]; +else + C_avg_temp=T_avg(:,nodes); +end + +% Translation Averaging +if(strcmp(TAMethod,'RLUD')) + [C_avg_temp,ed_ret_idx]=RLUD(edges_sg',-RT_sg,param,C_avg_temp); + tacost=LUDCost(RT_sg,edges_sg,C_avg_temp); +elseif(strcmp(TAMethod,'BATA')) + [C_avg_temp,ed_ret_idx]=BATA(edges_sg',-RT_sg,param,C_avg_temp,idxConst); + tacost=BATACost(RT_sg,edges_sg,C_avg_temp); +else + error('Invalid TA method'); +end + +% Converting back to original labels +T_avg=nan(3,maxImages); +T_avg(:,nodes)=C_avg_temp; +ed_ret_idx(edge_idx_map)=ed_ret_idx; + +end diff --git a/thirdparty/creta/RunTA.m b/thirdparty/creta/RunTA.m new file mode 100644 index 000000000..7ba2b5eb3 --- /dev/null +++ b/thirdparty/creta/RunTA.m @@ -0,0 +1,83 @@ +% Copyright (C) 2022 Computer Vision Lab, Electrical Engineering, +% Indian Institute of Science, Bengaluru, India. +% All rights reserved. +% +% Redistribution and use in source and binary forms, with or without +% modification, are permitted provided that the following conditions are +% met: +% +% * Redistributions of source code must retain the above copyright +% notice, this list of conditions and the following disclaimer. +% * Redistributions in binary form must reproduce the above +% copyright notice, this list of conditions and the following +% disclaimer in the documentation and/or other materials provided +% with the distribution. +% * Neither the name of Indian Institute of Science nor the +% names of its contributors may be used to endorse or promote products +% derived from this software without specific prior written permission. +% +% THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +% OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +% FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL +% THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +% LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +% FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER +% DEALINGS IN THE SOFTWARE. +% +% Author: Lalit Manam +% +% This file is a part of the implementation for the paper: +% Lalit Manam and Venu Madhav Govindu, Correspondence Reweighted +% Translation Averaging, Proceedings of the European Conference on +% Computer Vision 2022 + +function [T_avg,tacost,ed_ret_idx] = RunTA(edges,RT,T_avg,maxImages,param,TAMethod) +% Run Translation Averaging (TA) +% Inputs:: +% edges: MX2 matrix of camera pairs +% RT: 3XM matrix of relative translations +% T_avg: 3XN matrix of camera locations +% maxImages: Maximum no of images for indexing +% param: Parameters for TA Method +% TAMethod: 'RLUD' or 'BATA' +% Outputs: +% T_avg: 3XN matrix of camera locations +% tacost: Valur of TA cost at the solution +% ed_ret_idx: Edge indexes used for translation averaging + +% Relabelling nodes +nodes=unique(edges(:)); +G=graph(edges(:,1),edges(:,2),1:size(edges,1)); +D=degree(G); [~,loc]=max(D); +idxConst=find(nodes==loc); +G1=subgraph(G,nodes); +P=G1.Edges; +edges_sg=P.EndNodes; +idx=find(edges(:,1)>edges(:,2)); +edge_idx_map=P.Weight; +RT(:,idx)=-RT(:,idx); +RT_sg=RT(:,edge_idx_map); + +if(isempty(T_avg)) + C_avg_temp=[]; +else + C_avg_temp=T_avg(:,nodes); +end + +% Translation Averaging +if(strcmp(TAMethod,'RLUD')) + [C_avg_temp,ed_ret_idx]=RLUD(edges_sg',-RT_sg,param,C_avg_temp); + tacost=LUDCost(RT_sg,edges_sg,C_avg_temp); +elseif(strcmp(TAMethod,'BATA')) + [C_avg_temp,ed_ret_idx]=BATA(edges_sg',-RT_sg,param,C_avg_temp,idxConst); + tacost=BATACost(RT_sg,edges_sg,C_avg_temp); +else + error('Invalid TA method'); +end + +% Converting back to original labels +T_avg=nan(3,maxImages); +T_avg(:,nodes)=C_avg_temp; +ed_ret_idx(edge_idx_map)=ed_ret_idx; + +end From f89e45e177b5a7db039a7fa3db1f7536a7d10713 Mon Sep 17 00:00:00 2001 From: Travis Driver <travisdriver@gatech.edu> Date: Sat, 27 Jan 2024 14:46:07 -0500 Subject: [PATCH 2/2] Remove DKM --- thirdparty/DKM | 1 - 1 file changed, 1 deletion(-) delete mode 160000 thirdparty/DKM diff --git a/thirdparty/DKM b/thirdparty/DKM deleted file mode 160000 index d1cba4bf1..000000000 --- a/thirdparty/DKM +++ /dev/null @@ -1 +0,0 @@ -Subproject commit d1cba4bf1c96de1497aba031f709013404c08d5f