Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud
  Registration   In this paper, we propose the 3DFeat-Net which learns both 3D feature
detector and descriptor for point cloud matching using weak supervision. Unlike
many existing works, we do not require manual annotation of matching point
clusters. Instead, we leverage on alignment and attention mechanisms to learn
feature correspondences from GPS/INS tagged 3D point clouds without explicitly
specifying them. We create training and benchmark outdoor Lidar datasets, and
experiments show that 3DFeat-Net obtains state-of-the-art performance on these
gravity-aligned datasets.

12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
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The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin