Papers › PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation
PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation
Yisheng He, Wei Sun, Haibin Huang, Jianran Liu, Haoqiang Fan, Jian Sun
In this work, we present a novel data-driven method for robust 6DoF object pose estimation from a single RGBD image. Unlike previous methods that directly regressing pose parameters, we tackle this challenging task with a keypoint-based approach. Specifically, we propose a deep Hough voting network to detect 3D keypoints of objects and then estimate the 6D pose parameters within a least-squares fitting manner. Our method is a natural extension of 2D-keypoint approaches that successfully work on RGB based 6DoF estimation. It allows us to fully utilize the geometric constraint of rigid objects with the extra depth information and is easy for a network to learn and optimize. Extensive experiments were conducted to demonstrate the effectiveness of 3D-keypoint detection in the 6D pose estimation task. Experimental results also show our method outperforms the state-of-the-art methods by large margins on several benchmarks. Code and video are available at https://github.com/ethnhe/PVN3D.git.
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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 | LineMOD | PVN3D | Accuracy (ADD) | 99.4 | #2 of 5 | Archive leaderboard | report |
| 6D Pose Estimation | YCB-Video | PVN3D | ADDS AUC | 96.1 | #4 of 10 | Archive leaderboard | report |
| 6D Pose Estimation using RGBD | LineMOD | PVN3D | Mean ADD | 99.4 | #2 of 8 | Archive leaderboard | report |
| 6D Pose Estimation using RGBD | YCB-Video | PVN3D | Mean ADD-S | 95.5 | #5 of 9 | 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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