Download the KITTI Odometry Dataset (including color, velodyne laser data, and calibration files) and the annotations for Semantic Scene Completion from SemanticKITTI. Please follow the command image2depth_semantickitti to create depth maps and preprocess the annotations for semantic scene completion:
python tools/preprocess.py --kitti_root data/SemanticKITTI --kitti_preprocess_root data/SemanticKITTIThe data is organized in the following format:
/semantickittii/
|-- sequences/
│ |-- 00/
│ │ |-- poses.txt
│ │ |-- calib.txt
│ │ |-- image_2/
│ │ |-- image_3/
│ | |-- voxels/
│ | |- 000000.bin
│ | |- 000000.label
│ | |- 000000.occluded
│ | |- 000000.invalid
│ | |- 000005.bin
│ | |- 000005.label
│ | |- 000005.occluded
│ | |- 000005.invalid
│ |-- 01/
│ |-- 02/
│ .
│ |-- 21/
|-- labels/
│ |-- 00/
│ │ |-- 000000_1_1.npy
│ │ |-- 000000_1_2.npy
│ │ |-- 000005_1_1.npy
│ │ |-- 000005_1_2.npy
│ |-- 01/
│ .
│ |-- 10/
|-- lidarseg/
| |-- 00/
| │ |-- labels/
| | ├ 000001.label
| | ├ 000002.label
| |-- 01/
| |-- 02/
| .
| |-- 21/
|-- depth/sequences/
|-- 00/
│ |-- 000000.npy
| |-- 000001.npy
|-- 01/
|-- 02/
.
|-- 21/
Download the dataset from SSCBench-KITTI-360 and prepare the depth maps using image2depth_kitti360.
The data is organized in the following format:
/SSCBenchKITTI360/
|-- data_2d_raw
| |-- 2013_05_28_drive_0000_sync # train:[0, 2, 3, 4, 5, 7, 10] + val:[6] + test:[9]
| | |-- image_00
| | | |-- data_rect # RGB images for left camera
| | | | |-- 000000.png
| | | | |-- 000001.png
| | | | |-- ...
| | | |-- timestamps.txt
| | |-- image_01
| | | |-- data_rect # RGB images for right camera
| | | | |-- 000000.png
| | | | |-- 000001.png
| | | | |-- ...
| | | |-- timestamps.txt
| | |-- voxels # voxelized point clouds
| | | |-- 000000.bin # voxelized input
| | | |-- 000000.invalid # voxelized invalid mask
| | | |-- 000000.label #voxelized label
| | | |-- 000005.bin # calculate every 5 frames
| | | |-- 000005.invalid
| | | |-- 000005.label
| | | |-- ...
| | |-- cam0_to_world.txt
| | |-- pose.txt # car pose information
| |-- ...
| |-- 2013_05_28_drive_0010_sync
|-- labels
| |-- 2013_05_28_drive_0000_sync
| | |-- 000000_1_1.npy # original labels
| | |-- 000000_1_8.npy # 8x downsampled labels
| | |-- 000005_1_1.npy
| | |-- 000005_1_8.npy
| | |-- ...
| |-- ...
| |-- 2013_05_28_drive_0010_sync
|-- labels_half # not unified, downsampled
| |-- 2013_05_28_drive_0000_sync
| | |-- 000000_1_1.npy # original labels
| | |-- 000000_1_8.npy # 8x downsampled labels
| | |-- 000005_1_1.npy
| | |-- 000005_1_8.npy
| | |-- ...
| |-- ...
| |-- 2013_05_28_drive_0010_sync
|-- unified # unified
| |-- labels
| |-- 2013_05_28_drive_0000_sync
| | |-- 000000_1_1.npy # original labels
| | |-- 000000_1_8.npy # 8x downsampled labels
| | |-- 000005_1_1.npy
| | |-- 000005_1_8.npy
| | |-- ...
| |-- ...
| |-- 2013_05_28_drive_0010_sync
|-- calibration # preprocessed downsampled labels
| |-- calib_cam_to_pose.txt
| |-- calib_cam_to_velo.txt
| |-- calib_sick_to_velo.txt
| |-- image_02.yaml
| |-- image_03.yaml
| |-- perspective.txt
|-- depth
|-- sequences
|-- 2013_05_28_drive_0000_sync
| |-- 000000.npy
| |-- 000001.npy
|-- ...
|-- 2013_05_28_drive_0010_sync