Papers › 6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints
6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints
Chen Wang, Roberto Martín-Martín, Danfei Xu, Jun Lv, Cewu Lu, Li Fei-Fei, Silvio Savarese, Yuke Zhu
We present 6-PACK, a deep learning approach to category-level 6D object pose tracking on RGB-D data. Our method tracks in real-time novel object instances of known object categories such as bowls, laptops, and mugs. 6-PACK learns to compactly represent an object by a handful of 3D keypoints, based on which the interframe motion of an object instance can be estimated through keypoint matching. These keypoints are learned end-to-end without manual supervision in order to be most effective for tracking. Our experiments show that our method substantially outperforms existing methods on the NOCS category-level 6D pose estimation benchmark and supports a physical robot to perform simple vision-based closed-loop manipulation tasks. Our code and video are available at https://sites.google.com/view/6packtracking.
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Code
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Code Syntology ran Syntology
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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 RGBD | REAL275 | 6-PACK | Rerr | 16.0 | #11 of 11 | Archive leaderboard | report |
| 6D Pose Estimation using RGBD | REAL275 | 6-PACK | Terr | 3.5 | #11 of 11 | Archive leaderboard | report |
| 6D Pose Estimation using RGBD | REAL275 | 6-PACK | mAP 3DIou@25 | 94.2 | #11 of 11 | Archive leaderboard | report |
| 6D Pose Estimation using RGBD | REAL275 | 6-PACK | mAP 5, 5cm | 33.3 | #11 of 11 | 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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