Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Lepard: Learning partial point cloud matching in rigid and deformable
  scenes   We present Lepard, a Learning based approach for partial point cloud matching
in rigid and deformable scenes. The key characteristics are the following
techniques that exploit 3D positional knowledge for point cloud matching: 1) An
architecture that disentangles point cloud representation into feature space
and 3D position space. 2) A position encoding method that explicitly reveals 3D
relative distance information through the dot product of vectors. 3) A
repositioning technique that modifies the crosspoint-cloud relative positions.
Ablation studies demonstrate the effectiveness of the above techniques. In
rigid cases, Lepard combined with RANSAC and ICP demonstrates state-of-the-art
registration recall of 93.9% / 71.3% on the 3DMatch / 3DLoMatch. In deformable
cases, Lepard achieves +27.1% / +34.8% higher non-rigid feature matching recall
than the prior art on our newly constructed 4DMatch / 4DLoMatch benchmark.

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The categories of this paper are: nlin
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The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
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The categories of this paper are: patt-sol
18
The categories of this paper are: econ
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The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
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The categories of this paper are: supr-con
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The categories of this paper are: adap-org