Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints   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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The categories of this paper are: acc-phys
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The categories of this paper are: hep-lat
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The categories of this paper are: patt-sol
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The categories of this paper are: bayes-an
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The categories of this paper are: astro-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: nlin
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The categories of this paper are: hep-ex
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The categories of this paper are: q-fin
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The categories of this paper are: cond-mat
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The categories of this paper are: nucl-ex
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The categories of this paper are: physics
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The categories of this paper are: quant-ph