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6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints
It's deep learning approach to category-level 6D object pose tracking on RGB-D data. this 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 to be most effective for tracking. Their experiments show that the 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.
preprint: https://arxiv.org/abs/1910.10750
code: https://github.com/j96w/6-PACK
tweet: https://twitter.com/RobobertoMM/status/1187617487837257733?s=20
video: https://www.youtube.com/watch?v=INBjNZsnfy4
#CV #DL #PatternRecognition
It's deep learning approach to category-level 6D object pose tracking on RGB-D data. this 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 to be most effective for tracking. Their experiments show that the 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.
preprint: https://arxiv.org/abs/1910.10750
code: https://github.com/j96w/6-PACK
tweet: https://twitter.com/RobobertoMM/status/1187617487837257733?s=20
video: https://www.youtube.com/watch?v=INBjNZsnfy4
#CV #DL #PatternRecognition