From a069c934cb9c6cb0b291c6e4e67f72257ddb571f Mon Sep 17 00:00:00 2001 From: Robert Ennis Date: Tue, 14 May 2024 16:10:27 +0200 Subject: [PATCH 01/10] open PR to get tag-aligner alab article into staging --- alpha-lab/cards.json | 10 +++ alpha-lab/public/tag-aligner.webp | Bin 0 -> 694523 bytes .../tag-aligner/coord-sys-comparisons.png | Bin 0 -> 1764929 bytes alpha-lab/tag-aligner/index.md | 84 ++++++++++++++++++ alpha-lab/tag-aligner/observer_position.png | Bin 0 -> 1610968 bytes alpha-lab/tag-aligner/rim_3d_model.png | Bin 0 -> 1541700 bytes 6 files changed, 94 insertions(+) create mode 100644 alpha-lab/public/tag-aligner.webp create mode 100644 alpha-lab/tag-aligner/coord-sys-comparisons.png create mode 100644 alpha-lab/tag-aligner/index.md create mode 100644 alpha-lab/tag-aligner/observer_position.png create mode 100644 alpha-lab/tag-aligner/rim_3d_model.png diff --git a/alpha-lab/cards.json b/alpha-lab/cards.json index 8818eb396..90fa67d39 100644 --- a/alpha-lab/cards.json +++ b/alpha-lab/cards.json @@ -128,5 +128,15 @@ }, "image": "/alpha-lab/map-gaze-nadia.webp", "category": "Gaze Mapping" + }, + { + "title": "Map Gaze into a Reference 3D Coordinate System", + "details": "Map gaze, head pose, and observer position into a 3D coordinate system of your choice using our Tag Aligner tool.", + "link": { + "text": "View", + "href": "/alpha-lab/tag-aligner/" + }, + "image": "/alpha-lab/tag-aligner.webp", + "category": "Gaze Mapping" } ] diff --git a/alpha-lab/public/tag-aligner.webp b/alpha-lab/public/tag-aligner.webp new file mode 100644 index 0000000000000000000000000000000000000000..820ea2619247a35ae243b191b9a7ad05ce513ba4 GIT binary patch literal 694523 zcmV)BK*PU@P)qA%-$%)*q%3%F8Ji<5!$A0W<7E+{Ht zU|`GuvX#qA3P9{0AX_9U8_Zq+Viy4&2xM;ovC|>!6JRwE_8qXAkRWFu`vZ{8mjq@r zf$ZW-hp+`eY^c2;An2HqpOmTq^1X(krKN=?iGCmmN>VFIK(upyL1j^9dPa$YBQT^= z6+H8j^>h@B42(<|7(n3;QT;g(9G;9~XAoitfDoG_!NB0Zl!1Ze4?@hOmw{n{4g&-K zRD_tCAp?VeJ_EzPImx9(Wl&=nnT#122=M_S%Su>jvHbo3001~;SV?A0O#mtY000O8 z0f%V-1ONa40RR918UO$Q0007@0ssU60003100R%80{{d70003100I600001RaE=)O z06+jqL_t(|0qni^b7c3G<$2S417rdT?@`_ty(vnRW6SnztBodRdbVS>H+o{{hc)vD z@Xy$v_J^H_jftM#?u}6!iIgEF(HE;&-UBFjBr+4}J&^l3uUau04Jnn#q7wrHFcn%3>?TCdkNKR=H}!{+yU&Es+No~CuWZPV8t{{^EFi}&^O zny9tfO|#yv`FvjI^=^;HD&?9Li)E`;>wGr9c9~@|VVge`GILdJ=}y(^rGy3iaSpg= zfq>U)oI{Tn27*35p>CmQ*%FDCx!hj6b){|V%Uw%EV-^hf%;W3W)IPtZ2f}veX5BVc zYtH+4v5n6}Y;4?TrDEN3+q?WIX#L5k)vLRf&owOI4Ref$?Ro0fKVTM(do7nO+tyl} z*X-Hw@Ps|_=o2C%4x8H*v)#QObG2ihfXiZiVa9gXYQ=^% zYAtItnzp;U$F;?+(W;t{*G$c&E#4n9Kj)p@+OTlYZ{w2*>+LnHFA=m$uNJIc=vlK@ zvFXDJ3&y(6{I4$VTD|VFlaJ0@W~9$n7dAQXJu~KX;rfpG+!3xlXD+_4HtNh}m-%wp z*hI>T<*KzCJqvmx=JR@NYkS*Tja_TiyA}>J_UR~}QD-fg`B*3O(-Y?6zhEe2-MyBT zDpgzC+^}Z1Vv+s;^We9=uFIONmj>&?%e)L{rfhz8zumcgi!n-DW@5i}-5b`Q&T~#p zOC}<$*{->Itl6$HpH;L%k8$yLY$AwwP)+=TNVt5EE?mSeSQmt!WIhn zEzuX_@1Whfxyahq>qC~y=Pc15vcUnrd3}4f$^2*6+w1|Kv(AGNzco8G&cns;J=SS8 z&D-;^A6U<<_c~({@CWSZi6rOYwrs9ymD;w|D!aDZ4x4MwWz|Z{8J_@aCCvUC7>HUp z%KG)NUpOzVp+>cB@rch(KAhyWExUNWY-iutu}<5|dee2YhFvb(bMHF)I2ejptJSt( zB;c%#zNF8#H@ntthV0~H&)Cfy*Vqd?cH~5iwN$h0MxFK4WqFnc3p!xJ-B$X>HO*5@OS zf5*P{+#fM!tM=kgeqfJ3`nWyu(4*F2dHu)#=Wpzt%RjQatGBJwX)qUVbN9^R?8Cvq zIP1Sg@Kg)Z^S^H5t zfBp^27Tvb*z(ZClt=sC_MfT95)hjDDFgU>6tl0kPK}&dMYS3(4=E9a5=(qf4(cXHq%zCTZE*CXDImf+b!_s3u z))M!tlHbP0j@iHYSO3;##wVFmuX))g9u|m>fA5Qn{^#%Zj5%nwY-M%B{`UL-pWV25 z-ZUsf>2W){|A5_IUbKz;itSpHcl7SfJ8RXi1;+%iEp4Z;W9zHy%wx}zNwObAMyzF*uT18d=|bwOV|2Y zn9uvU#!up$zu$9uzwf)Bak}5vcrv3y&sU3nfIs9y%^p5Afy(D0Kli74v#{P0Z&!NZL&ih#sJE4$Pf4^+x4s4a2FTkq|f3p zKwh_Qk(k#a9IssN0l+v{j-T7ATRva6B#hzIV^7=AaG%v0n}DYh*Ck*TX9>c_}~$n0wB!I9k)|Ye9?}deALEA#sH-WfMd(@+YOGx74n2h%QVm(WZ0MY|Bh@#JC_oZzo*WnMO0-)RN zG%Y;@X@UTG{XSb+&f8A5W$9EJK(o!bnzfp3KxBjC0mgm1Rw=eE8Vp-@Gizfbv*v*` zu{Z#kW)t&afF^*p7k~r|SO_2-fJC@i18vq-1F#ZcKKh0Lxk1PY-}kic_-vr01IU^X z*G4^Wxm=!~cmVS$EAK#hZ*SVxY8FtOvn*g|X(?~jGHa=|YdggjK!`QRd3hj~BCBP{ zYL)r&xB=2zML@X6BH=iD0K&rhZ$pZLdOd410^zNRq;ajqor)zG>juZ`xnq!~G3JP2 z2i%MJ`g%cgv9?&%5GB^@I)oTPAc7a~bJ_T`&rUx)YF96BS`~ur0VHp4)vVogaV`N{ zUCHy=%st2JODC=8oNI}nxd3@P)@;IvxNatwHOu}S^V^jR5Uz^fc^@!<_0!LsPp~!v z5aKETu*5ug93s)Ev*>oc?9aF}$394Zt5M`w2Ep@Lxl{w#-6QvlxAFk-4#!Eb2c~R! 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name: twitter:width + content: "1280" + - name: twitter:height + content: "720" + - property: og:image + content: "https://i.ytimg.com/vi/nt_zNSBMJWI/maxresdefault.jpg" +tags: [Neon, Cloud] +--- + + + +# Map Gaze into a Reference 3D Coordinate System + + + + + +::: tip +Experience your participants' journey and gaze direction in a real-world environment, digitally reimagined, using Tag Aligner! Our new tool combines Neon eye tracking and headpose data with a user-supplied third-party 3D model. +::: + +## Real-world Positions and Rotations + +![Comparison of the different ways to project Neon data into various coordinate systems](./coord-sys-comparisons.png) + +It's often important to know the position of an observer in an environment, and how they orient their head and gaze while navigating it. For example, to understand when museum-goers pause in thought while viewing artworks, or how engineers move about and gaze at the surrounding environment during mission-critical tasks. + +With a digital twin of a real-world environment, you can visualize an observer's trajectory, and how they direct their gaze spatially onto objects within the scene, by mapping Neon’s gaze + head pose into the digital twin. + +In this guide, we'll show you how to do this using data from Neon + Reference Image Mapper (RIM) and the digital twin of your choice. + +## Transforming Poses from RIM Data + +The first step, as in the title, is to transform camera poses from the RIM 3D model to the Digital twin. For context, our RIM enrichment uses 3D features of a scene to map gaze onto a reference image regardless of the subject’s position or orientation. Under the hood, RIM builds a sparse 3D model of the environment and calculates camera poses relative to the 3D features of the scene. However, the origin, scaling, and orientation of the coordinate system for these camera poses is arbitrary and cannot be used directly for any real-world metrics or visualizations. + +![Depiction of the sparse 3D model produced by our Reference Image Mapper](./rim_3d_model.png) + +The white dots on this image (statue of [Theodor Koch-Grünberg](https://en.wikipedia.org/wiki/Theodor_Koch-Grunberg)) represent key points of a sparse 3D model built from a RIM enrichment scanning recording. The model is used by RIM to calculate scene camera positions in an arbitrary coordinate system. + +Rather usefully, we found that a stationary AprilTag marker with a known size, position, and rotation, placed in a RIM-enriched recording, can be used to align the camera poses to a useful coordinate system, such as that of a digital twin or real-world measures. So we implemented this in a transformation function, and put it into a package called ‘Tag Aligner’. + +After calculating the transformation to convert from the RIM enrichment’s arbitrary coordinate system to your desired coordinate system, the transformation can then be applied to all of the recordings’ poses from that enrichment. + +These aligned poses could be used for analysis or to visualize observer motion and gaze within a digital twin. The Tag Aligner package includes a simple 3D viewer that can load a glTF model and allow you to explore the scene while playing back the recording. A set of Neon frames and the gaze ray are also added to the scene for visualization. + +## Steps to recreate + +1. AprilTags are the key to Tag Aligner, hence the name, so make sure you have one printed and at the ready! You will want a tag from the “tag36h11” family, printed at a good, visible size. We have already prepared [a PDF of them](https://github.com/pupil-labs/pupil-helpers/blob/master/markers_stickersheet/tag36h11_full.pdf?raw=True) for you. Note that you need to include a white border around the printed AprilTag. +2. Grab a copy of [Tag Aligner](https://github.com/pupil-labs/tag-aligner) and follow the instructions in the README. +3. If you have a glTF model of your environment and want to visualize the aligned poses and gaze, then be sure to check out the “Bonus” section of the Tag Aligner repo, where we offer a real-time visualization, a Blender plugin, and a Python notebook with some basic analysis. + + +## Working with Aligned Poses + +You have now expanded the analysis possibilities of Neon + RIM to the third dimension! + +After running the Tag Aligner tool, you will find a file in the recording folder, called "aligned_poses.csv", with the scaled and aligned poses of the scene camera over time. + +If you run the bonus section, you will also have a pop-up interactive window that renders a glTF model of your environment that you can use to visualize the aligned poses and gaze. + +Finally, you can analyze the results further to gain other insights. For example, you might want to plot an overhead view of the wearer’s trajectory. To get you started, we plotted the “translation_z” against the “translation_x” columns - check out the result below. + +![Overhead projection of observer trajectory and gaze mapped onto statue scene](./observer_position.png) + +## Related Content + +Be sure to check out our AlphaLab article about how to [Map Gaze Onto a 3D Model of an Environment](https://docs.pupil-labs.com/alpha-lab/nerfs/) using Neural Radiance Fields. + +::: tip +Need assistance with aligning your AprilTags or applying the transformations to your RIM recordings? Or do you have something more custom in mind? Reach out to us via email at [info@pupil-labs.com](mailto:info@pupil-labs.com), on our [Discord server](https://pupil-labs.com/chat/), or visit our [Support Page](https://pupil-labs.com/products/support/) for dedicated support options. +::: \ No newline at end of file diff --git a/alpha-lab/tag-aligner/observer_position.png b/alpha-lab/tag-aligner/observer_position.png new file mode 100644 index 0000000000000000000000000000000000000000..8b623a22c0afcf2ee659e044925d1a51d6a75033 GIT binary patch literal 1610968 zcmeEu`8%6=`?fCAc66OVOI3ARJEcXV_G(LOi9I4nv?W?gs3nMXwA1#ftz!v8EVWk> zQ6WJjbQzQswM0sgww6?slpwb7-hJNh_j%sG;QeWG$lKd=w@OIrqF1o&kbVd);>65FA*Tvx9a>*j*h(<#TA@AV1%~2@98y&_4I$ zxhuNDJW^tpgoO2V3)kp^Z(?hrvS$K2erGs2GODU)CKA%wRW@7b4I-1%%CPXkZT^Qz2S zQX*^azSIku?&+S;0pft;n5dt^#5%BK7B^w z(Eq+BA)#O)A^YDgqX!Zf{<{^ich{}|Zhbwl`_+H9KK!!x_x~RC`MCoUd;as{l^e$- zenk2IK7LRC=Y;$AS`MM~I8xQ4f z3^#%@sbsAc&9)`YjcM;~q-pf`Pmj15USZU7z4>PS#%AJ@D}5@XU)Y`8-?6;h4|(E8?{>tmAhXl#(hs}UBcFWP2BF=!Bhx#-aXs;SAv+G 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zmY3|`|7`ku@peFh9E1Qp@NYVGaAHw+E#c>)=4bV%Wuy3|{sgx0=+? zCN*|FxHE4xI#NMcZCSQp5KbrFYK8<;#y9IpAGVwA=zzq7c{7*53p=`PyMV-r*`NRL zFRDFz5LrsPZ{H&o?6RTs@MHC#&0ue?4@h{+10UZ#HU$4T0f~3t&D#MgAaO3RUa7rC z4F0afKCtz#W^+$_aoJC5-8!$9{mlwU@E`bzc+1`ew*wNbFQX`kLCWWwA+ah;w*?>% z9z0Z&n%e)w0SPn3a|LbrWMe5mS)xO>)x=OTvO;%qW6Z$U+McY3M;?7N?I&|6Hk8bo zjI+e~RF*Tpn6cH5e)Qukf4=y4FCbAIF^AmSUvFdJHU@5E;5G(sW8gLhZe!p!25w{E rHU@5E;5G(sW8gLhZe!rH#=w6BtPOFQ=kc^j00000NkvXXu0mjfuuS`o literal 0 HcmV?d00001 From bd5ccbcec53284844a6b0e11190fa4f17e79fba7 Mon Sep 17 00:00:00 2001 From: Robert Ennis Date: Wed, 15 May 2024 15:33:59 +0200 Subject: [PATCH 02/10] resolve nmt comments --- alpha-lab/tag-aligner/index.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/alpha-lab/tag-aligner/index.md b/alpha-lab/tag-aligner/index.md index d307655b3..1a8b39017 100644 --- a/alpha-lab/tag-aligner/index.md +++ b/alpha-lab/tag-aligner/index.md @@ -40,7 +40,7 @@ It's often important to know the position of an observer in an environment, and With a digital twin of a real-world environment, you can visualize an observer's trajectory, and how they direct their gaze spatially onto objects within the scene, by mapping Neon’s gaze + head pose into the digital twin. -In this guide, we'll show you how to do this using data from Neon + Reference Image Mapper (RIM) and the digital twin of your choice. +In this guide, we'll show you how to do this using data from Neon + Reference Image Mapper (RIM) and the digital twin of your choice. If you're not already familiar with our RIM enrichment, be sure to check out [the RIM documentation](https://docs.pupil-labs.com/neon/pupil-cloud/enrichments/reference-image-mapper/). ## Transforming Poses from RIM Data @@ -58,7 +58,7 @@ These aligned poses could be used for analysis or to visualize observer motion a ## Steps to recreate -1. AprilTags are the key to Tag Aligner, hence the name, so make sure you have one printed and at the ready! You will want a tag from the “tag36h11” family, printed at a good, visible size. We have already prepared [a PDF of them](https://github.com/pupil-labs/pupil-helpers/blob/master/markers_stickersheet/tag36h11_full.pdf?raw=True) for you. Note that you need to include a white border around the printed AprilTag. +1. AprilTags are the key to Tag Aligner, hence the name, so make sure you have one printed and at the ready! You will want a tag from the “tag36h11” family. We have already prepared [a PDF of them](https://github.com/pupil-labs/pupil-helpers/blob/master/markers_stickersheet/tag36h11_full.pdf?raw=True) for you. The marker will need to be clearly visible to the scene camera for this tool to work, so you'll need to think about the necessary size of the marker for your environment. Note that you need to include a white border around the printed AprilTag. 2. Grab a copy of [Tag Aligner](https://github.com/pupil-labs/tag-aligner) and follow the instructions in the README. 3. If you have a glTF model of your environment and want to visualize the aligned poses and gaze, then be sure to check out the “Bonus” section of the Tag Aligner repo, where we offer a real-time visualization, a Blender plugin, and a Python notebook with some basic analysis. @@ -69,7 +69,7 @@ You have now expanded the analysis possibilities of Neon + RIM to the third dime After running the Tag Aligner tool, you will find a file in the recording folder, called "aligned_poses.csv", with the scaled and aligned poses of the scene camera over time. -If you run the bonus section, you will also have a pop-up interactive window that renders a glTF model of your environment that you can use to visualize the aligned poses and gaze. +If you run the bonus section, you will also have a pop-up interactive window that renders a glTF model of your environment that you can use to visualize the aligned poses and gaze. We also provide a Blender Add-on to import the Tag Aligner data as an animation track. It is found in [the Github repo](https://github.com/pupil-labs/tag-aligner). Finally, you can analyze the results further to gain other insights. For example, you might want to plot an overhead view of the wearer’s trajectory. To get you started, we plotted the “translation_z” against the “translation_x” columns - check out the result below. From a77eff50c9369cf29805c427fbf5550d40faace2 Mon Sep 17 00:00:00 2001 From: N-M-T Date: Mon, 20 May 2024 11:48:43 -0400 Subject: [PATCH 03/10] @N-M-T's edits --- alpha-lab/.vitepress/config.mts | 1 + alpha-lab/cards.json | 2 +- alpha-lab/tag-aligner/index.md | 41 ++++++++++++++++++++++----------- 3 files changed, 29 insertions(+), 15 deletions(-) diff --git a/alpha-lab/.vitepress/config.mts b/alpha-lab/.vitepress/config.mts index b768819fe..d5ba84546 100644 --- a/alpha-lab/.vitepress/config.mts +++ b/alpha-lab/.vitepress/config.mts @@ -12,6 +12,7 @@ let theme_config_additions = { { text: "Map Gaze Onto Dynamic Screen Content", link: "/map-your-gaze-to-a-2d-screen/" }, { text: "Map Gaze Onto Body Parts", link: "/dense-pose/" }, { text: "Map Gaze Onto a 3D Model of an Environment", link: "/nerfs/" }, + { text: "Map Gaze Into a User-Supplied 3D Model", link: "/tag-aligner/" }, { text: "Map Gaze Onto Facial Landmarks", link: "/gaze-on-face/" }, ], }, diff --git a/alpha-lab/cards.json b/alpha-lab/cards.json index 90fa67d39..52bcc967d 100644 --- a/alpha-lab/cards.json +++ b/alpha-lab/cards.json @@ -130,7 +130,7 @@ "category": "Gaze Mapping" }, { - "title": "Map Gaze into a Reference 3D Coordinate System", + "title": "Map Gaze into a User-Supplied 3D Model", "details": "Map gaze, head pose, and observer position into a 3D coordinate system of your choice using our Tag Aligner tool.", "link": { "text": "View", diff --git a/alpha-lab/tag-aligner/index.md b/alpha-lab/tag-aligner/index.md index 1a8b39017..182e5d244 100644 --- a/alpha-lab/tag-aligner/index.md +++ b/alpha-lab/tag-aligner/index.md @@ -1,5 +1,5 @@ --- -title: "Map Gaze into a Reference 3D Coordinate System" +title: "Map Gaze Into a User-Supplied 3D Model" description: "Map gaze, head pose, and observer position into a 3D coordinate system of your choice using our Tag Aligner tool." permalink: /alpha-lab/tag-aligner/ meta: @@ -8,7 +8,7 @@ meta: - name: twitter:image content: "https://i.ytimg.com/vi/nt_zNSBMJWI/maxresdefault.jpg" - name: twitter:player - content: "https://www.youtube.com/embed/vEshPxgWs3E" + content: "https://www.youtube.com/embed/jag9EQB7840" - name: twitter:width content: "1280" - name: twitter:height @@ -22,14 +22,14 @@ tags: [Neon, Cloud] import TagLinks from '@components/TagLinks.vue' -# Map Gaze into a Reference 3D Coordinate System +# Map Gaze Into a User-Supplied 3D Model - + ::: tip -Experience your participants' journey and gaze direction in a real-world environment, digitally reimagined, using Tag Aligner! Our new tool combines Neon eye tracking and headpose data with a user-supplied third-party 3D model. +Experience your participants' journey and gaze direction in a real-world environment, digitally reimagined! Use Tag Aligner to combine Neon eye tracking and headpose data with a 3D model of your choice. ::: ## Real-world Positions and Rotations @@ -38,29 +38,42 @@ Experience your participants' journey and gaze direction in a real-world environ It's often important to know the position of an observer in an environment, and how they orient their head and gaze while navigating it. For example, to understand when museum-goers pause in thought while viewing artworks, or how engineers move about and gaze at the surrounding environment during mission-critical tasks. -With a digital twin of a real-world environment, you can visualize an observer's trajectory, and how they direct their gaze spatially onto objects within the scene, by mapping Neon’s gaze + head pose into the digital twin. +With a 3D model of a real-world environment, you can visualize an observer's trajectory, and how they direct their gaze +spatially onto objects within the scene, by mapping Neon’s gaze + head pose into the 3D model. -In this guide, we'll show you how to do this using data from Neon + Reference Image Mapper (RIM) and the digital twin of your choice. If you're not already familiar with our RIM enrichment, be sure to check out [the RIM documentation](https://docs.pupil-labs.com/neon/pupil-cloud/enrichments/reference-image-mapper/). +In this guide, we'll show you how to do this using data from Neon + Reference Image Mapper (RIM) and the 3D model of your choice. If you're not already familiar with our RIM enrichment, be sure to check out [the RIM documentation](https://docs.pupil-labs.com/neon/pupil-cloud/enrichments/reference-image-mapper/). ## Transforming Poses from RIM Data +The first step is to create a successful RIM enrichment within the environment of interest. Subsequently, +the camera poses from the RIM 3D model are transformed to a user-supplied 3D model. -The first step, as in the title, is to transform camera poses from the RIM 3D model to the Digital twin. For context, our RIM enrichment uses 3D features of a scene to map gaze onto a reference image regardless of the subject’s position or orientation. Under the hood, RIM builds a sparse 3D model of the environment and calculates camera poses relative to the 3D features of the scene. However, the origin, scaling, and orientation of the coordinate system for these camera poses is arbitrary and cannot be used directly for any real-world metrics or visualizations. +For context, our RIM enrichment uses 3D features +of a scene to map gaze onto a reference image regardless of the subject’s position or orientation. Under the hood, RIM +builds a sparse 3D model of the environment and calculates camera poses relative to it. However, +the origin, scaling, and orientation of the coordinate system for these camera poses is arbitrary and cannot be used directly +for any real-world metrics or visualizations. Thus, building a transform between the RIM model and a user-supplied model +can enable richer visualizations and open up a wealth of analysis possibilities. ![Depiction of the sparse 3D model produced by our Reference Image Mapper](./rim_3d_model.png) -The white dots on this image (statue of [Theodor Koch-Grünberg](https://en.wikipedia.org/wiki/Theodor_Koch-Grunberg)) represent key points of a sparse 3D model built from a RIM enrichment scanning recording. The model is used by RIM to calculate scene camera positions in an arbitrary coordinate system. +The white dots on this image (statue of [Theodor Koch-Grünberg](https://en.wikipedia.org/wiki/Theodor_Koch-Grunberg)) represent +key points of the sparse 3D model built by the RIM enrichment. The model is used by RIM to calculate scene camera positions +in an arbitrary coordinate system. -Rather usefully, we found that a stationary AprilTag marker with a known size, position, and rotation, placed in a RIM-enriched recording, can be used to align the camera poses to a useful coordinate system, such as that of a digital twin or real-world measures. So we implemented this in a transformation function, and put it into a package called ‘Tag Aligner’. +By placing a stationary AprilTag marker with a known size, position, and rotation, in a RIM-enriched recording, and applying some +math, we can determine a transformation that aligns the camera poses to a useful coordinate system, such as that of a user-supplied 3D model. -After calculating the transformation to convert from the RIM enrichment’s arbitrary coordinate system to your desired coordinate system, the transformation can then be applied to all of the recordings’ poses from that enrichment. +After calculating the transformation to convert from the RIM enrichment’s arbitrary coordinate system to the desired 3D model, +the transformation can then be applied to all of the recordings’ poses from that enrichment. So essentially, the AprilTag marker +only needs to be present in one recording. -These aligned poses could be used for analysis or to visualize observer motion and gaze within a digital twin. The Tag Aligner package includes a simple 3D viewer that can load a glTF model and allow you to explore the scene while playing back the recording. A set of Neon frames and the gaze ray are also added to the scene for visualization. +The aligned poses can then be used for analysis or to visualize observer motion and gaze within the 3D model! ## Steps to recreate -1. AprilTags are the key to Tag Aligner, hence the name, so make sure you have one printed and at the ready! You will want a tag from the “tag36h11” family. We have already prepared [a PDF of them](https://github.com/pupil-labs/pupil-helpers/blob/master/markers_stickersheet/tag36h11_full.pdf?raw=True) for you. The marker will need to be clearly visible to the scene camera for this tool to work, so you'll need to think about the necessary size of the marker for your environment. Note that you need to include a white border around the printed AprilTag. +1. AprilTags are the key to Tag Aligner, hence the name, so make sure you have one printed and at the ready! You will want a tag from the “tag36h11” family. We have already prepared [a PDF of them](https://github.com/pupil-labs/pupil-helpers/blob/master/markers_stickersheet/tag36h11_full.pdf?raw=True) for you. The marker will need to be clearly visible to the scene camera for this tool to work, so you'll need to think about the necessary size of the marker for your environment. Make sure to include a white border around the printed AprilTag. 2. Grab a copy of [Tag Aligner](https://github.com/pupil-labs/tag-aligner) and follow the instructions in the README. -3. If you have a glTF model of your environment and want to visualize the aligned poses and gaze, then be sure to check out the “Bonus” section of the Tag Aligner repo, where we offer a real-time visualization, a Blender plugin, and a Python notebook with some basic analysis. +3. If you want to visualize the aligned poses and gaze, then be sure to check out the “Bonus” section of the Tag Aligner repo, where we offer a real-time visualization, a Blender plugin, and a Python notebook with some basic analysis. ## Working with Aligned Poses From e8857ca7549db2fe695af615fa617324a4b0c59b Mon Sep 17 00:00:00 2001 From: Robert Ennis Date: Tue, 4 Jun 2024 13:05:11 +0200 Subject: [PATCH 04/10] incorporate latest suggestions/comments --- alpha-lab/tag-aligner/index.md | 28 +++++++--------------------- 1 file changed, 7 insertions(+), 21 deletions(-) diff --git a/alpha-lab/tag-aligner/index.md b/alpha-lab/tag-aligner/index.md index 182e5d244..9fcb41d32 100644 --- a/alpha-lab/tag-aligner/index.md +++ b/alpha-lab/tag-aligner/index.md @@ -36,42 +36,28 @@ Experience your participants' journey and gaze direction in a real-world environ ![Comparison of the different ways to project Neon data into various coordinate systems](./coord-sys-comparisons.png) -It's often important to know the position of an observer in an environment, and how they orient their head and gaze while navigating it. For example, to understand when museum-goers pause in thought while viewing artworks, or how engineers move about and gaze at the surrounding environment during mission-critical tasks. +It’s often useful to know the position of an observer in an environment and how they orient their head and gaze while navigating it. For example, consider museum goers viewing artwork. Knowing their precise position and gaze direction, and being able to replay their actions, can provide vital insights into how they engage with different pieces, whether they follow a particular sequence, or how they interact with informational displays. This may help identify areas for improving exhibit design and optimizing the overall visitor experience, in addition to shedding light on how people choose to experience museums, or other spaces, in general. -With a 3D model of a real-world environment, you can visualize an observer's trajectory, and how they direct their gaze -spatially onto objects within the scene, by mapping Neon’s gaze + head pose into the 3D model. +Given a 3D model of a real-world environment, you can map gaze and head-pose data from Neon into the 3D model. This allows you to visualize the observers trajectory and how they direct their gaze within the scene. In this guide, we'll show you how to do this using data from Neon + Reference Image Mapper (RIM) and the 3D model of your choice. If you're not already familiar with our RIM enrichment, be sure to check out [the RIM documentation](https://docs.pupil-labs.com/neon/pupil-cloud/enrichments/reference-image-mapper/). ## Transforming Poses from RIM Data -The first step is to create a successful RIM enrichment within the environment of interest. Subsequently, -the camera poses from the RIM 3D model are transformed to a user-supplied 3D model. -For context, our RIM enrichment uses 3D features -of a scene to map gaze onto a reference image regardless of the subject’s position or orientation. Under the hood, RIM -builds a sparse 3D model of the environment and calculates camera poses relative to it. However, -the origin, scaling, and orientation of the coordinate system for these camera poses is arbitrary and cannot be used directly -for any real-world metrics or visualizations. Thus, building a transform between the RIM model and a user-supplied model -can enable richer visualizations and open up a wealth of analysis possibilities. +For context, our RIM enrichment uses 3D features of a scene to map gaze onto a reference image. Under the hood, RIM builds a sparse 3D model of the environment and calculates camera poses relative to it. However, the origin, scaling, and orientation of the coordinate system for these camera poses is arbitrary (e.g., they are not specified in meters or feet). Thus, building a transform between the RIM model and a user-supplied model can enable richer visualizations and open up a wealth of analysis possibilities. ![Depiction of the sparse 3D model produced by our Reference Image Mapper](./rim_3d_model.png) +Figure 1. The white dots on this image (statue of [Theodor Koch-Grünberg](https://en.wikipedia.org/wiki/Theodor_Koch-Grunberg)) represent key points of a sparse 3D model built from a RIM enrichment scanning recording. The model is used by RIM to calculate scene camera positions in an arbitrary coordinate system. -The white dots on this image (statue of [Theodor Koch-Grünberg](https://en.wikipedia.org/wiki/Theodor_Koch-Grunberg)) represent -key points of the sparse 3D model built by the RIM enrichment. The model is used by RIM to calculate scene camera positions -in an arbitrary coordinate system. +By placing a stationary AprilTag marker with a known size, position, and rotation, in a RIM-enriched recording, we can determine a transformation that aligns the camera poses to your coordinate system, -By placing a stationary AprilTag marker with a known size, position, and rotation, in a RIM-enriched recording, and applying some -math, we can determine a transformation that aligns the camera poses to a useful coordinate system, such as that of a user-supplied 3D model. - -After calculating the transformation to convert from the RIM enrichment’s arbitrary coordinate system to the desired 3D model, -the transformation can then be applied to all of the recordings’ poses from that enrichment. So essentially, the AprilTag marker -only needs to be present in one recording. +The AprilTag marker only needs to be present in one recording, to compute the transformation. It can then be applied to all other recordings from that Enrichment. The aligned poses can then be used for analysis or to visualize observer motion and gaze within the 3D model! ## Steps to recreate -1. AprilTags are the key to Tag Aligner, hence the name, so make sure you have one printed and at the ready! You will want a tag from the “tag36h11” family. We have already prepared [a PDF of them](https://github.com/pupil-labs/pupil-helpers/blob/master/markers_stickersheet/tag36h11_full.pdf?raw=True) for you. The marker will need to be clearly visible to the scene camera for this tool to work, so you'll need to think about the necessary size of the marker for your environment. Make sure to include a white border around the printed AprilTag. +1. AprilTags are the key to Tag Aligner, hence the name, so make sure you have one printed and at the ready! You will want a tag from the “tag36h11” family. We have already prepared [a PDF of them](https://github.com/pupil-labs/pupil-helpers/blob/master/markers_stickersheet/tag36h11_full.pdf?raw=True) for you. The marker will need to be clearly visible to the scene camera for this tool to work, so you'll need to make it large enough. Make sure to include a white border around the printed AprilTag. 2. Grab a copy of [Tag Aligner](https://github.com/pupil-labs/tag-aligner) and follow the instructions in the README. 3. If you want to visualize the aligned poses and gaze, then be sure to check out the “Bonus” section of the Tag Aligner repo, where we offer a real-time visualization, a Blender plugin, and a Python notebook with some basic analysis. From a1573838617e4f3e0bc520ffcda59a01badcb009 Mon Sep 17 00:00:00 2001 From: Robert Ennis Date: Tue, 4 Jun 2024 13:42:21 +0200 Subject: [PATCH 05/10] fix link in inline HTML --- alpha-lab/tag-aligner/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/alpha-lab/tag-aligner/index.md b/alpha-lab/tag-aligner/index.md index 9fcb41d32..584a8fdb0 100644 --- a/alpha-lab/tag-aligner/index.md +++ b/alpha-lab/tag-aligner/index.md @@ -47,7 +47,7 @@ In this guide, we'll show you how to do this using data from Neon + Reference Im For context, our RIM enrichment uses 3D features of a scene to map gaze onto a reference image. Under the hood, RIM builds a sparse 3D model of the environment and calculates camera poses relative to it. However, the origin, scaling, and orientation of the coordinate system for these camera poses is arbitrary (e.g., they are not specified in meters or feet). Thus, building a transform between the RIM model and a user-supplied model can enable richer visualizations and open up a wealth of analysis possibilities. ![Depiction of the sparse 3D model produced by our Reference Image Mapper](./rim_3d_model.png) -Figure 1. The white dots on this image (statue of [Theodor Koch-Grünberg](https://en.wikipedia.org/wiki/Theodor_Koch-Grunberg)) represent key points of a sparse 3D model built from a RIM enrichment scanning recording. The model is used by RIM to calculate scene camera positions in an arbitrary coordinate system. +Figure 1. The white dots on this image (statue of Theodor Koch-Grünberg) represent key points of a sparse 3D model built from a RIM enrichment scanning recording. The model is used by RIM to calculate scene camera positions in an arbitrary coordinate system. By placing a stationary AprilTag marker with a known size, position, and rotation, in a RIM-enriched recording, we can determine a transformation that aligns the camera poses to your coordinate system, From d01759c3f6e96edb6ffe2087593d768beba8f732 Mon Sep 17 00:00:00 2001 From: Robert Ennis Date: Tue, 4 Jun 2024 13:53:11 +0200 Subject: [PATCH 06/10] formatting fixes --- alpha-lab/tag-aligner/index.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/alpha-lab/tag-aligner/index.md b/alpha-lab/tag-aligner/index.md index 584a8fdb0..2cfc8d28a 100644 --- a/alpha-lab/tag-aligner/index.md +++ b/alpha-lab/tag-aligner/index.md @@ -42,27 +42,27 @@ Given a 3D model of a real-world environment, you can map gaze and head-pose dat In this guide, we'll show you how to do this using data from Neon + Reference Image Mapper (RIM) and the 3D model of your choice. If you're not already familiar with our RIM enrichment, be sure to check out [the RIM documentation](https://docs.pupil-labs.com/neon/pupil-cloud/enrichments/reference-image-mapper/). -## Transforming Poses from RIM Data +## Transforming Poses From RIM Data -For context, our RIM enrichment uses 3D features of a scene to map gaze onto a reference image. Under the hood, RIM builds a sparse 3D model of the environment and calculates camera poses relative to it. However, the origin, scaling, and orientation of the coordinate system for these camera poses is arbitrary (e.g., they are not specified in meters or feet). Thus, building a transform between the RIM model and a user-supplied model can enable richer visualizations and open up a wealth of analysis possibilities. +For context, our RIM enrichment uses 3D features of a scene to map gaze onto a reference image. Under the hood, RIM builds a sparse 3D model of the environment and calculates camera poses relative to it. However, the origin, scaling, and orientation of the coordinate system for these camera poses is arbitrary (e.g., they are not specified in meters or feet). Thus, building a transformation between the RIM model and a user-supplied model can enable richer visualizations and open up a wealth of analysis possibilities. ![Depiction of the sparse 3D model produced by our Reference Image Mapper](./rim_3d_model.png) Figure 1. The white dots on this image (statue of Theodor Koch-Grünberg) represent key points of a sparse 3D model built from a RIM enrichment scanning recording. The model is used by RIM to calculate scene camera positions in an arbitrary coordinate system. -By placing a stationary AprilTag marker with a known size, position, and rotation, in a RIM-enriched recording, we can determine a transformation that aligns the camera poses to your coordinate system, +By placing a stationary AprilTag marker with a known size, position, and rotation, in a RIM-enriched recording, we can determine a transformation that aligns the camera poses to your coordinate system. The AprilTag marker only needs to be present in one recording, to compute the transformation. It can then be applied to all other recordings from that Enrichment. The aligned poses can then be used for analysis or to visualize observer motion and gaze within the 3D model! -## Steps to recreate +## Steps To Recreate 1. AprilTags are the key to Tag Aligner, hence the name, so make sure you have one printed and at the ready! You will want a tag from the “tag36h11” family. We have already prepared [a PDF of them](https://github.com/pupil-labs/pupil-helpers/blob/master/markers_stickersheet/tag36h11_full.pdf?raw=True) for you. The marker will need to be clearly visible to the scene camera for this tool to work, so you'll need to make it large enough. Make sure to include a white border around the printed AprilTag. 2. Grab a copy of [Tag Aligner](https://github.com/pupil-labs/tag-aligner) and follow the instructions in the README. 3. If you want to visualize the aligned poses and gaze, then be sure to check out the “Bonus” section of the Tag Aligner repo, where we offer a real-time visualization, a Blender plugin, and a Python notebook with some basic analysis. -## Working with Aligned Poses +## Working With Aligned Poses You have now expanded the analysis possibilities of Neon + RIM to the third dimension! From 68a1daa8022cdbc1d49e63731fbd32dba47f6e63 Mon Sep 17 00:00:00 2001 From: Robert Ennis Date: Wed, 5 Jun 2024 12:26:33 +0200 Subject: [PATCH 07/10] formatting fixes --- alpha-lab/cards.json | 2 +- alpha-lab/tag-aligner/index.md | 14 +++++++------- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/alpha-lab/cards.json b/alpha-lab/cards.json index 52bcc967d..bcee08839 100644 --- a/alpha-lab/cards.json +++ b/alpha-lab/cards.json @@ -130,7 +130,7 @@ "category": "Gaze Mapping" }, { - "title": "Map Gaze into a User-Supplied 3D Model", + "title": "Map Gaze Into a User-Supplied 3D Model", "details": "Map gaze, head pose, and observer position into a 3D coordinate system of your choice using our Tag Aligner tool.", "link": { "text": "View", diff --git a/alpha-lab/tag-aligner/index.md b/alpha-lab/tag-aligner/index.md index 2cfc8d28a..08860c5ef 100644 --- a/alpha-lab/tag-aligner/index.md +++ b/alpha-lab/tag-aligner/index.md @@ -40,16 +40,16 @@ It’s often useful to know the position of an observer in an environment and ho Given a 3D model of a real-world environment, you can map gaze and head-pose data from Neon into the 3D model. This allows you to visualize the observers trajectory and how they direct their gaze within the scene. -In this guide, we'll show you how to do this using data from Neon + Reference Image Mapper (RIM) and the 3D model of your choice. If you're not already familiar with our RIM enrichment, be sure to check out [the RIM documentation](https://docs.pupil-labs.com/neon/pupil-cloud/enrichments/reference-image-mapper/). +In this guide, we'll show you how to do this using data from Neon + Reference Image Mapper and the 3D model of your choice. If you're not already familiar with our Reference Image Mapper enrichment, be sure to check out [the Reference Image Mapper documentation](https://docs.pupil-labs.com/neon/pupil-cloud/enrichments/reference-image-mapper/). -## Transforming Poses From RIM Data +## Transforming Poses From Reference Image Mapper Data -For context, our RIM enrichment uses 3D features of a scene to map gaze onto a reference image. Under the hood, RIM builds a sparse 3D model of the environment and calculates camera poses relative to it. However, the origin, scaling, and orientation of the coordinate system for these camera poses is arbitrary (e.g., they are not specified in meters or feet). Thus, building a transformation between the RIM model and a user-supplied model can enable richer visualizations and open up a wealth of analysis possibilities. +For context, our Reference Image Mapper enrichment uses 3D features of a scene to map gaze onto a reference image. Under the hood, Reference Image Mapper builds a sparse 3D model of the environment and calculates camera poses relative to it. However, the origin, scaling, and orientation of the coordinate system for these camera poses is arbitrary (e.g., they are not specified in meters or feet). Thus, building a transformation between the Reference Image Mapper model and a user-supplied model can enable richer visualizations and open up a wealth of analysis possibilities. ![Depiction of the sparse 3D model produced by our Reference Image Mapper](./rim_3d_model.png) -Figure 1. The white dots on this image (statue of Theodor Koch-Grünberg) represent key points of a sparse 3D model built from a RIM enrichment scanning recording. The model is used by RIM to calculate scene camera positions in an arbitrary coordinate system. +Figure 1. The white dots on this image (statue of Theodor Koch-Grünberg) represent key points of a sparse 3D model built from a Reference Image Mapper enrichment scanning recording. The model is used by Reference Image Mapper to calculate scene camera positions in an arbitrary coordinate system. -By placing a stationary AprilTag marker with a known size, position, and rotation, in a RIM-enriched recording, we can determine a transformation that aligns the camera poses to your coordinate system. +By placing a stationary AprilTag marker with a known size, position, and rotation, in a Reference Image Mapper-enriched recording, we can determine a transformation that aligns the camera poses to your coordinate system. The AprilTag marker only needs to be present in one recording, to compute the transformation. It can then be applied to all other recordings from that Enrichment. @@ -64,7 +64,7 @@ The aligned poses can then be used for analysis or to visualize observer motion ## Working With Aligned Poses -You have now expanded the analysis possibilities of Neon + RIM to the third dimension! +You have now expanded the analysis possibilities of Neon + Reference Image Mapper to the third dimension! After running the Tag Aligner tool, you will find a file in the recording folder, called "aligned_poses.csv", with the scaled and aligned poses of the scene camera over time. @@ -79,5 +79,5 @@ Finally, you can analyze the results further to gain other insights. For example Be sure to check out our AlphaLab article about how to [Map Gaze Onto a 3D Model of an Environment](https://docs.pupil-labs.com/alpha-lab/nerfs/) using Neural Radiance Fields. ::: tip -Need assistance with aligning your AprilTags or applying the transformations to your RIM recordings? Or do you have something more custom in mind? Reach out to us via email at [info@pupil-labs.com](mailto:info@pupil-labs.com), on our [Discord server](https://pupil-labs.com/chat/), or visit our [Support Page](https://pupil-labs.com/products/support/) for dedicated support options. +Need assistance with aligning your AprilTags or applying the transformations to your Reference Image Mapper recordings? Or do you have something more custom in mind? Reach out to us via email at [info@pupil-labs.com](mailto:info@pupil-labs.com), on our [Discord server](https://pupil-labs.com/chat/), or visit our [Support Page](https://pupil-labs.com/products/support/) for dedicated support options. ::: \ No newline at end of file From 8e331ffa46fa397997b1fd5d090eef6abd46fa1f Mon Sep 17 00:00:00 2001 From: Robert Ennis Date: Wed, 5 Jun 2024 12:55:58 +0200 Subject: [PATCH 08/10] slightly more visible JAN render and gaze circle shown as aligned to surrounding surface normals --- alpha-lab/public/tag-aligner.webp | Bin 694523 -> 705470 bytes .../tag-aligner/coord-sys-comparisons.png | Bin 1764929 -> 1777536 bytes 2 files changed, 0 insertions(+), 0 deletions(-) diff --git a/alpha-lab/public/tag-aligner.webp b/alpha-lab/public/tag-aligner.webp index 820ea2619247a35ae243b191b9a7ad05ce513ba4..859e1bf84dca1c3cb6066777024fd608fa1e2aad 100644 GIT binary patch literal 705470 zcmV)OK(@b$P)}CqA%-$%)*q%3%F8Ji<5!$A0W<7E+{Ht zU|`GuvX#qA3P9{0AX_9U8_Zq+Viy4&2xM;ovC|>!6JRwE_8qXAkRWFu`vZ{8mjq@r zf$ZW-hp+`eY^c2;An2HqpOmTq^1X(krKN=?iGCmmN>VFIK(upyL1j^9dPa$YBQT^= z6+H8j^>h@B42(<|7(n3;QT;g(9G;9~XAoitfDoG_!NB0Zl!1Ze4?@hOmw{n{4g&-K zRD_tCAp?VeJ_EzPImx9(Wl&=nnT#122=M^{|4K-kOWcqE001~;SV?A0O#mtY000O8 z0f%V-1ONa40RR918UO$Q0007@0ssU60003100R)90{{d70003100IC20002cGq@uF z06+jqL_t(|0qnhZlVw+S-MOFl-sDSfsxr$LJQ|HQB)e%uCvwD)hSG>J`fnm;V!i`D z%uIwLBuz{VH6f8~lI_tjjRMM>tV(a6_x`>4ty=;GVwwaRs6wNgl^xYp$b5Otx#ym< z*4}IF?fQ5B``4zGQpeh@kp%-G>vcy~uQ%;Ce{ap^7yWkccF`U@Xjn87vgy<;KNq%O zcxtEnEo)ZY=JkfH*Kb>^*0n@3V3l&)s;500j7Am?_-!&CnYpGmJDa!BaA>2^#HN!e ze})!GrYsPT*=Rhma4=wgpT~UOsRcuRb9p>gtu(Dttyt-}W`VHZ+U>8VrUUe`MoH*Is-%X6dYp&${eK->q7+&h<=(mg2nOu-j%9 z{Pylo_G~y9TOtv+v+Fr)wyHM28s~nz*6mE~=FN_sSxehw7_x)Crj4h4yLkDMoxgn5 zZr%8E^9RiuwKC(Av{5&1e(%JVm(E$h6|z(|Wxi0s(z#{paa=RECDJ)ty>!khTaU~i z@!R3i9s4i;`MY+&ea6#qbGyeDjJR#(%#xiP-?Bmfz$TMApY@u@?Xf}6YscGNJKpOs zUOwv$2IdcY7+;r-`cumnVwOmGcs?2pmyL~kwgKb7J;%e`hu=1?=B-xQwcISn^tdb$ z%~-oO<~|xW@eUZfj>S_^3v+Wn`CipJ9kbbmsO4rNmgV?*CSGsQe*C>nbB#il%O&jI zgDs21yw+*=7^A6Oc&=czX3OfwBg>~UwzG3&XU|@;M!jLpM%j`Xm#tq;*p2r}<{Ct8 zHlMMeKVX5Nmoe~KxmLAyw`HE_(9AcmR;O>R#>m{vlVN{sgVunl<+XsvYx!Kpj!x>f zFu!K`g;hH|*y4EK&_= zyNqFk$3Eg(-FA9XW3Kg?Gal}9Vsk4YTV9FS#>S%kr+kU|=*<^eN{9c@AKenU8 zvZd1rizkCNx16*7sBfb|*CqqAPPbVezyJN7RZD)WH@n

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z)E4xsBd6u+#{pCuzOjVWTtJglS9%j>Y!+G#YFlpjF2E^Nzsi^CLB@Jd<*sFv+)x@% zO1k1*XW7IG7hH-N;U3&_u$Q%a;g+E#Hi&p%6&2gRqDQCK&!sv__czw@^Jv8f;q?*n z+i4$_`l}oHrwRRj{v>pc{-0edILj}ZRPx-vJEcEgt%Vd$P(ee80KtFwu0MUuH0fEA zIc=(x%HJ8lPw$r}CDE48#!avEH>dIUCnvu~Gt*atI(PnW_YHY=%X5Jamh}p6(Er`9 w{(f)IZ=aMY;rrj^`rqaH*IE64d%AY<=L1--XZAMq9)f?eQcAb7Zru0$A6w@bvH$=8 From 547fad8428bb762f5cca0f9102f9b0992a2017ec Mon Sep 17 00:00:00 2001 From: Robert Ennis Date: Wed, 5 Jun 2024 12:58:55 +0200 Subject: [PATCH 09/10] updated youtube link for video with more visible JAN and gaze circle sur norm alignment --- alpha-lab/tag-aligner/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/alpha-lab/tag-aligner/index.md b/alpha-lab/tag-aligner/index.md index 08860c5ef..4a5a69632 100644 --- a/alpha-lab/tag-aligner/index.md +++ b/alpha-lab/tag-aligner/index.md @@ -26,7 +26,7 @@ import TagLinks from '@components/TagLinks.vue' - + ::: tip Experience your participants' journey and gaze direction in a real-world environment, digitally reimagined! Use Tag Aligner to combine Neon eye tracking and headpose data with a 3D model of your choice. From 285086e9cf9f0863d8a73e942b46ef229736b7cf Mon Sep 17 00:00:00 2001 From: Robert Ennis Date: Wed, 5 Jun 2024 16:50:17 +0200 Subject: [PATCH 10/10] attempt to fix conflict with web aois commit --- alpha-lab/cards.json | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/alpha-lab/cards.json b/alpha-lab/cards.json index bcee08839..780507724 100644 --- a/alpha-lab/cards.json +++ b/alpha-lab/cards.json @@ -129,6 +129,16 @@ "image": "/alpha-lab/map-gaze-nadia.webp", "category": "Gaze Mapping" }, + { + "title": "Map Gaze Onto Website AOIs", + "details": "Define areas of interest on a website and map gaze onto them using our Web-AOI tool.", + "link": { + "text": "View", + "href": "/alpha-lab/web-aois/" + }, + "image": "/alpha-lab/web-aoi.webp", + "category": "Gaze Mapping" + }, { "title": "Map Gaze Into a User-Supplied 3D Model", "details": "Map gaze, head pose, and observer position into a 3D coordinate system of your choice using our Tag Aligner tool.",

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