Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: REGTR: End-to-end Point Cloud Correspondences with Transformers   Despite recent success in incorporating learning into point cloud
registration, many works focus on learning feature descriptors and continue to
rely on nearest-neighbor feature matching and outlier filtering through RANSAC
to obtain the final set of correspondences for pose estimation. In this work,
we conjecture that attention mechanisms can replace the role of explicit
feature matching and RANSAC, and thus propose an end-to-end framework to
directly predict the final set of correspondences. We use a network
architecture consisting primarily of transformer layers containing self and
cross attentions, and train it to predict the probability each point lies in
the overlapping region and its corresponding position in the other point cloud.
The required rigid transformation can then be estimated directly from the
predicted correspondences without further post-processing. Despite its
simplicity, our approach achieves state-of-the-art performance on 3DMatch and
ModelNet benchmarks. Our source code can be found at
https://github.com/yewzijian/RegTR .

7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat