Papers › Occlusion-Robust Object Pose Estimation with Holistic Representation
Occlusion-Robust Object Pose Estimation with Holistic Representation
Bo Chen, Tat-Jun Chin, Marius Klimavicius
Practical object pose estimation demands robustness against occlusions to the target object. State-of-the-art (SOTA) object pose estimators take a two-stage approach, where the first stage predicts 2D landmarks using a deep network and the second stage solves for 6DOF pose from 2D-3D correspondences. Albeit widely adopted, such two-stage approaches could suffer from novel occlusions when generalising and weak landmark coherence due to disrupted features. To address these issues, we develop a novel occlude-and-blackout batch augmentation technique to learn occlusion-robust deep features, and a multi-precision supervision architecture to encourage holistic pose representation learning for accurate and coherent landmark predictions. We perform careful ablation tests to verify the impact of our innovations and compare our method to SOTA pose estimators. Without the need of any post-processing or refinement, our method exhibits superior performance on the LINEMOD dataset. On the YCB-Video dataset our method outperforms all non-refinement methods in terms of the ADD(-S) metric. We also demonstrate the high data-efficiency of our method. Our code is available at http://github.com/BoChenYS/ROPE
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 6D Pose Estimation using RGB | LineMOD | ROPE | Accuracy (ADD) | 95.61% | #7 of 22 | Archive leaderboard | report |
| 6D Pose Estimation using RGB | LineMOD | ROPE | Mean ADD | 95.61 | #7 of 22 | Archive leaderboard | report |
| 6D Pose Estimation using RGB | Occlusion LineMOD | ROPE | Mean ADD | 45.95 | #10 of 13 | Archive leaderboard | report |
| 6D Pose Estimation using RGB | YCB-Video | ROPE | Mean ADD | 66.59 | #2 of 5 | Archive leaderboard | report |
| 6D Pose Estimation using RGB | YCB-Video | ROPE | Mean AUC | 79.88 | #2 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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