Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 3D-Rotation-Equivariant Quaternion Neural Networks   This paper proposes a set of rules to revise various neural networks for 3D
point cloud processing to rotation-equivariant quaternion neural networks
(REQNNs). We find that when a neural network uses quaternion features under
certain conditions, the network feature naturally has the rotation-equivariance
property. Rotation equivariance means that applying a specific rotation
transformation to the input point cloud is equivalent to applying the same
rotation transformation to all intermediate-layer quaternion features. Besides,
the REQNN also ensures that the intermediate-layer features are invariant to
the permutation of input points. Compared with the original neural network, the
REQNN exhibits higher rotation robustness.

30
The categories of this paper are: q-alg
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn