Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SPNet: Multi-Shell Kernel Convolution for Point Cloud Semantic
  Segmentation   Feature encoding is essential for point cloud analysis. In this paper, we
propose a novel point convolution operator named Shell Point Convolution
(SPConv) for shape encoding and local context learning. Specifically, SPConv
splits 3D neighborhood space into shells, aggregates local features on manually
designed kernel points, and performs convolution on the shells. Moreover,
SPConv incorporates a simple yet effective attention module that enhances local
feature aggregation. Based upon SPConv, a deep neural network named SPNet is
constructed to process large-scale point clouds. Poisson disk sampling and
feature propagation are incorporated in SPNet for better efficiency and
accuracy. We provided details of the shell design and conducted extensive
experiments on challenging large-scale point cloud datasets. Experimental
results show that SPConv is effective in local shape encoding, and our SPNet is
able to achieve top-ranking performances in semantic segmentation tasks.

28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ