Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Geometric Attention for Prediction of Differential Properties in 3D
  Point Clouds   Estimation of differential geometric quantities in discrete 3D data
representations is one of the crucial steps in the geometry processing
pipeline. Specifically, estimating normals and sharp feature lines from raw
point cloud helps improve meshing quality and allows us to use more precise
surface reconstruction techniques. When designing a learnable approach to such
problems, the main difficulty is selecting neighborhoods in a point cloud and
incorporating geometric relations between the points. In this study, we present
a geometric attention mechanism that can provide such properties in a learnable
fashion. We establish the usefulness of the proposed technique with several
experiments on the prediction of normal vectors and the extraction of feature
lines.

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The categories of this paper are: dg-ga
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The categories of this paper are: solv-int
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
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The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph