Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning and Matching Multi-View Descriptors for Registration of Point
  Clouds   Critical to the registration of point clouds is the establishment of a set of
accurate correspondences between points in 3D space. The correspondence problem
is generally addressed by the design of discriminative 3D local descriptors on
the one hand, and the development of robust matching strategies on the other
hand. In this work, we first propose a multi-view local descriptor, which is
learned from the images of multiple views, for the description of 3D keypoints.
Then, we develop a robust matching approach, aiming at rejecting outlier
matches based on the efficient inference via belief propagation on the defined
graphical model. We have demonstrated the boost of our approaches to
registration on the public scanning and multi-view stereo datasets. The
superior performance has been verified by the intensive comparisons against a
variety of descriptors and matching methods.

26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ