6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints

23 Oct 2019  ·  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. read more

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
6D Pose Estimation NOCS-REAL275 6-PACK 5°5 cm 33.3 # 1
IOU25 94.2 # 1
Rerr 16.0 # 1
Terr 3.5 # 1


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