Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A-CNN: Annularly Convolutional Neural Networks on Point Clouds   Analyzing the geometric and semantic properties of 3D point clouds through
the deep networks is still challenging due to the irregularity and sparsity of
samplings of their geometric structures. This paper presents a new method to
define and compute convolution directly on 3D point clouds by the proposed
annular convolution. This new convolution operator can better capture the local
neighborhood geometry of each point by specifying the (regular and dilated)
ring-shaped structures and directions in the computation. It can adapt to the
geometric variability and scalability at the signal processing level. We apply
it to the developed hierarchical neural networks for object classification,
part segmentation, and semantic segmentation in large-scale scenes. The
extensive experiments and comparisons demonstrate that our approach outperforms
the state-of-the-art methods on a variety of standard benchmark datasets (e.g.,
ModelNet10, ModelNet40, ShapeNet-part, S3DIS, and ScanNet).

2
The categories of this paper are: math
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom