Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Closest Point: Learning Representations for Point Cloud
  Registration   Point cloud registration is a key problem for computer vision applied to
robotics, medical imaging, and other applications. This problem involves
finding a rigid transformation from one point cloud into another so that they
align. Iterative Closest Point (ICP) and its variants provide simple and
easily-implemented iterative methods for this task, but these algorithms can
converge to spurious local optima. To address local optima and other
difficulties in the ICP pipeline, we propose a learning-based method, titled
Deep Closest Point (DCP), inspired by recent techniques in computer vision and
natural language processing. Our model consists of three parts: a point cloud
embedding network, an attention-based module combined with a pointer generation
layer, to approximate combinatorial matching, and a differentiable singular
value decomposition (SVD) layer to extract the final rigid transformation. We
train our model end-to-end on the ModelNet40 dataset and show in several
settings that it performs better than ICP, its variants (e.g., Go-ICP, FGR),
and the recently-proposed learning-based method PointNetLK. Beyond providing a
state-of-the-art registration technique, we evaluate the suitability of our
learned features transferred to unseen objects. We also provide preliminary
analysis of our learned model to help understand whether domain-specific and/or
global features facilitate rigid registration.

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The categories of this paper are: nlin
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The categories of this paper are: q-fin
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The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: mtrl-th
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The categories of this paper are: atom-ph
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The categories of this paper are: econ
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The categories of this paper are: dg-ga
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The categories of this paper are: supr-con
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-lat
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The categories of this paper are: gr-qc
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The categories of this paper are: chao-dyn
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The categories of this paper are: adap-org
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The categories of this paper are: chem-ph