Research map / 3D vision / Human pose estimation
Global Hypothesis Generation for 6D Object Pose Estimation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use fully connected CRF global geometry reasoning instead of Brachmann et al.'s sampling-based hypothesis generation | 6D object pose estimation with the shared random forest · Occluded Object Dataset | average pose accuracy: 76.7% vs 56.6%; 20.1% | Helped |
| Use fully connected CRF global geometry reasoning instead of Krull et al.'s sampling-based hypothesis generation | 6D object pose estimation with the shared random forest · Occluded Object Dataset | average pose accuracy: 76.7% vs 70.3%; 6.4% | Helped |
| Use fully connected CRF global geometry reasoning rather than Hinterstoisser et al.'s method | 6D object pose estimation · Occluded Object Dataset | average pose accuracy: 76.7% vs 76.2%; 0.4% | Helped |
Closest papers by meaning
- Pos3R: 6D Pose Estimation for Unseen Objects Made Easy (CVPR 2025 · similarity 0.88)
- 3D-Aware Hypothesis & Verification for Generalizable Relative Object Pose Estimation (ICLR 2024 · similarity 0.88)
- Open-Vocabulary Object 6D Pose Estimation (CVPR 2024 · similarity 0.87)
- Any6D: Model-free 6D Pose Estimation of Novel Objects (CVPR 2025 · similarity 0.87)
- SingRef6D: Monocular Novel Object Pose Estimation with a Single RGB Reference (NeurIPS 2025 · similarity 0.87)
- UA-Pose: Uncertainty-Aware 6D Object Pose Estimation and Online Object Completion with Partial References (CVPR 2025 · similarity 0.87)
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