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Research map / 3D vision / Human pose estimation

Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning

NeurIPS 2018 · 3D vision › Human pose estimation · approach: Category-level object pose estimation · 130 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace annotated keypoint supervision with end-to-end pose and multi-view consistency objectivesKeypointNet versus supervised keypoint baseline · ShapeNet cars testmean angular distance error: 13.961 -> 11.310Helped
Replace annotated keypoint supervision with end-to-end pose and multi-view consistency objectivesKeypointNet versus supervised keypoint baseline · ShapeNet chairs testmean angular distance error: 20.502 -> 14.572Helped
Replace annotated keypoint supervision with end-to-end pose and multi-view consistency objectivesKeypointNet versus supervised keypoint baseline · ShapeNet planes testmean angular distance error: 17.800 -> 17.330Helped
Add a predicted orientation flagSupervised keypoint baseline · ShapeNet cars, planes, and chairs testmean angular distance error: 16.268 -> 13.961; 18.350 -> 17.800; 21.882 -> 20.502Helped
Remove the multi-view consistency loss (ablation)KeypointNet · ShapeNet keypoint tracking across viewskeypoint consistencyHurt
Remove the pose estimation loss (ablation)KeypointNet · ShapeNet keypoint selectionkeypoint spatial distributionHurt
Increase noise in the pose estimation loss (ablation)KeypointNet · ShapeNet keypoint selectionkeypoint spatial distributionHelped
Remove silhouette consistency (ablation)KeypointNet · ShapeNet keypoint locationssilhouette adherenceHurt
Vary the number of predicted keypoints (ablation)KeypointNet · ShapeNet object categoriescoverage of object partsHelped

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