Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Online Adaptation for Implicit Object Tracking and Shape Reconstruction
  in the Wild   Tracking and reconstructing 3D objects from cluttered scenes are the key
components for computer vision, robotics and autonomous driving systems. While
recent progress in implicit function (e.g., DeepSDF) has shown encouraging
results on high-quality 3D shape reconstruction, it is still very challenging
to generalize to cluttered and partially observable LiDAR data. In this paper,
we propose to leverage the continuity in video data. We introduce a novel and
unified framework which utilizes a DeepSDF model to simultaneously track and
reconstruct 3D objects in the wild. We online adapt the DeepSDF model in the
video, iteratively improving the shape reconstruction while in return improving
the tracking, and vice versa. We experiment with both Waymo and KITTI datasets,
and show significant improvements over state-of-the-art methods for both
tracking and shape reconstruction.

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The categories of this paper are: cmp-lg
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The categories of this paper are: adap-org
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The categories of this paper are: hep-lat
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The categories of this paper are: funct-an
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The categories of this paper are: q-bio
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The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-th
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The categories of this paper are: q-alg
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The categories of this paper are: math-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: ao-sci
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The categories of this paper are: alg-geom
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The categories of this paper are: cond-mat
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The categories of this paper are: nlin