OneViewAll achieves 92.5% ADD-0.1 accuracy on LINEMOD for novel object 6D pose estimation using only one real reference view by integrating category, symmetry, and patch-level semantic priors in a projection-equivariant alignment.
So- pose: Exploiting self-occlusion for direct 6d pose estimation
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MAPRPose reports 76.5% Average Recall on the BOP benchmark for multi-object 6D pose estimation, beating FoundationPose by 3.1% while running 43 times faster through mask-aware proposals and amodal refinement.
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OneViewAll: Semantic Prior Guided One-View 6D Pose Estimation for Novel Objects
OneViewAll achieves 92.5% ADD-0.1 accuracy on LINEMOD for novel object 6D pose estimation using only one real reference view by integrating category, symmetry, and patch-level semantic priors in a projection-equivariant alignment.
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MAPRPose: Mask-Aware Proposal and Amodal Refinement for Multi-Object 6D Pose Estimation
MAPRPose reports 76.5% Average Recall on the BOP benchmark for multi-object 6D pose estimation, beating FoundationPose by 3.1% while running 43 times faster through mask-aware proposals and amodal refinement.