Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning a Local Feature Descriptor for 3D LiDAR Scans   Robust data association is necessary for virtually every SLAM system and
finding corresponding points is typically a preprocessing step for scan
alignment algorithms. Traditionally, handcrafted feature descriptors were used
for these problems but recently learned descriptors have been shown to perform
more robustly. In this work, we propose a local feature descriptor for 3D LiDAR
scans. The descriptor is learned using a Convolutional Neural Network (CNN).
Our proposed architecture consists of a Siamese network for learning a feature
descriptor and a metric learning network for matching the descriptors. We also
present a method for estimating local surface patches and obtaining
ground-truth correspondences. In extensive experiments, we compare our learned
feature descriptor with existing 3D local descriptors and report highly
competitive results for multiple experiments in terms of matching accuracy and
computation time. \end{abstract}

22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an