Papers › Segmentation-driven 6D Object Pose Estimation
Segmentation-driven 6D Object Pose Estimation
Yinlin Hu, Joachim Hugonot, Pascal Fua, Mathieu Salzmann
The most recent trend in estimating the 6D pose of rigid objects has been to train deep networks to either directly regress the pose from the image or to predict the 2D locations of 3D keypoints, from which the pose can be obtained using a PnP algorithm. In both cases, the object is treated as a global entity, and a single pose estimate is computed. As a consequence, the resulting techniques can be vulnerable to large occlusions. In this paper, we introduce a segmentation-driven 6D pose estimation framework where each visible part of the objects contributes a local pose prediction in the form of 2D keypoint locations. We then use a predicted measure of confidence to combine these pose candidates into a robust set of 3D-to-2D correspondences, from which a reliable pose estimate can be obtained. We outperform the state-of-the-art on the challenging Occluded-LINEMOD and YCB-Video datasets, which is evidence that our approach deals well with multiple poorly-textured objects occluding each other. Furthermore, it relies on a simple enough architecture to achieve real-time performance.
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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 | Occlusion LineMOD | SegDriven | Mean ADD | 27 | #12 of 13 | Archive leaderboard | report |
| 6D Pose Estimation using RGB | YCB-Video | SegDriven | Accuracy (ADD) | 39.0% | #4 of 5 | Archive leaderboard | report |
| 6D Pose Estimation using RGB | YCB-Video | SegDriven | Mean ADD | 39 | #4 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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