Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stereo Neural Vernier Caliper   We propose a new object-centric framework for learning-based stereo 3D object
detection. Previous studies build scene-centric representations that do not
consider the significant variation among outdoor instances and thus lack the
flexibility and functionalities that an instance-level model can offer. We
build such an instance-level model by formulating and tackling a local update
problem, i.e., how to predict a refined update given an initial 3D cuboid
guess. We demonstrate how solving this problem can complement scene-centric
approaches in (i) building a coarse-to-fine multi-resolution system, (ii)
performing model-agnostic object location refinement, and (iii) conducting
stereo 3D tracking-by-detection. Extensive experiments demonstrate the
effectiveness of our approach, which achieves state-of-the-art performance on
the KITTI benchmark. Code and pre-trained models are available at
https://github.com/Nicholasli1995/SNVC.

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The categories of this paper are: funct-an
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The categories of this paper are: solv-int
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The categories of this paper are: comp-gas
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The categories of this paper are: acc-phys
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The categories of this paper are: physics
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The categories of this paper are: chao-dyn
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The categories of this paper are: atom-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: cond-mat
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The categories of this paper are: math-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: patt-sol