Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 3DRegNet: A Deep Neural Network for 3D Point Registration   We present 3DRegNet, a novel deep learning architecture for the registration
of 3D scans. Given a set of 3D point correspondences, we build a deep neural
network to address the following two challenges: (i) classification of the
point correspondences into inliers/outliers, and (ii) regression of the motion
parameters that align the scans into a common reference frame. With regard to
regression, we present two alternative approaches: (i) a Deep Neural Network
(DNN) registration and (ii) a Procrustes approach using SVD to estimate the
transformation. Our correspondence-based approach achieves a higher speedup
compared to competing baselines. We further propose the use of a refinement
network, which consists of a smaller 3DRegNet as a refinement to improve the
accuracy of the registration. Extensive experiments on two challenging datasets
demonstrate that we outperform other methods and achieve state-of-the-art
results. The code is available.

4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin