Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Noise-resistant Deep Learning for Object Classification in 3D Point
  Clouds Using a Point Pair Descriptor   Object retrieval and classification in point cloud data is challenged by
noise, irregular sampling density and occlusion. To address this issue, we
propose a point pair descriptor that is robust to noise and occlusion and
achieves high retrieval accuracy. We further show how the proposed descriptor
can be used in a 4D convolutional neural network for the task of object
classification. We propose a novel 4D convolutional layer that is able to learn
class-specific clusters in the descriptor histograms. Finally, we provide
experimental validation on 3 benchmark datasets, which confirms the superiority
of the proposed approach.

25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
16
The categories of this paper are: stat
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex