Papers › Segmentation-driven 6D Object Pose Estimation

Segmentation-driven 6D Object Pose Estimation

6 Dec 2018CVPR 2019 6arXiv:1812.02541archive 2025-07-28

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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cvlab-epfl/segmentation-driven-pose officialmentioned in papermentioned on GitHubpytorchGPL-3.0 report
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cvlab-epfl/single-stage-pose mentioned on GitHubpytorchGPL-3.0 report
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add_err AP-EPFL/DA-segmentation-driven-pose/metric.py community (archive-listed) unverified MIT (permissive) · a8996657dfd0bebd · report
adds_err AP-EPFL/DA-segmentation-driven-pose/metric.py community (archive-listed) unverified MIT (permissive) · 89e617351e50af88 · report
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Tasks

6D Pose Estimation6D Pose Estimation using RGBObjectPose EstimationPose PredictionSegmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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

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