Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: (Just) A Spoonful of Refinements Helps the Registration Error Go Down   We tackle data-driven 3D point cloud registration. Given point
correspondences, the standard Kabsch algorithm provides an optimal rotation
estimate. This allows to train registration models in an end-to-end manner by
differentiating the SVD operation. However, given the initial rotation estimate
supplied by Kabsch, we show we can improve point correspondence learning during
model training by extending the original optimization problem. In particular,
we linearize the governing constraints of the rotation matrix and solve the
resulting linear system of equations. We then iteratively produce new solutions
by updating the initial estimate. Our experiments show that, by plugging our
differentiable layer to existing learning-based registration methods, we
improve the correspondence matching quality. This yields up to a 7% decrease in
rotation error for correspondence-based data-driven registration methods.

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The categories of this paper are: acc-phys
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The categories of this paper are: q-fin
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The categories of this paper are: eess
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
35
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: quant-ph
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
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The categories of this paper are: funct-an
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The categories of this paper are: math-ph
10
The categories of this paper are: q-bio