Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DeltaConv: Anisotropic Geometric Deep Learning with Exterior Calculus   Learning from 3D point-cloud data has rapidly gained momentum, motivated by
the success of deep learning on images and the increased availability of 3D
data. In this paper, we aim to construct anisotropic convolutions that work
directly on the surface derived from a point cloud. This is challenging because
of the lack of a global coordinate system for tangential directions on
surfaces. We introduce a new convolution operator called DeltaConv, which
combines geometric operators from exterior calculus to enable the construction
of anisotropic filters on point clouds. Because these operators are defined on
scalar- and vector-fields, we separate the network into a scalar- and a
vector-stream, which are connected by the operators. The vector stream enables
the network to explicitly represent, evaluate, and process directional
information. Our convolutions are robust and simple to implement and show
improved accuracy compared to state-of-the-art approaches on several
benchmarks, while also speeding up training and inference.

5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
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The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph