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Pose Synchronization Under Multiple Pair-Wise Relative Poses

CVPR 2023 · 3D vision › Human pose estimation · approach: Learned camera pose estimation · 2 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use diffusion-generated candidate poses and joint pose selection instead of K-BestPose synchronization · ScanNet, 1 relative pose per pairrotation estimates with error under 10° (%): 56.4% vs. 17.3% lower for K-BestHelped
Use diffusion-generated candidate poses and joint pose selection instead of K-BestPose synchronization · ScanNet, 1 relative pose per pairtranslation estimates with error under 0.25m (%): 58.9%, 4.6% higher than K-BestHelped
Use diffusion-generated candidate poses and joint pose selection instead of K-BestPose synchronization · ScanNet, up to 4 relative poses per pairrotation estimates with error under 10° (%): 22.1% higher than 67.8%Helped
Use diffusion-generated candidate poses and joint pose selection instead of K-BestPose synchronization · ScanNet, up to 4 relative poses per pairtranslation estimates with error under 0.25m (%): 12.5% higher than 73.2%Helped
Add Gauss-Newton pose refinement (Step III) (ablation)Ours (Step I + II) · 18-scan reconstruction examplemean rotation error: 2.96° -> 2.08°Helped
Add Gauss-Newton pose refinement (Step III) (ablation)Ours (Step I + II) · 18-scan reconstruction examplemean translation error: 0.12 -> 0.06Helped

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