Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CGS-Net: Aggregating Colour, Geometry and Semantic Features for
  Large-Scale Indoor Place Recognition   We describe an approach to large-scale indoor place recognition that
aggregates low-level colour and geometric features with high-level semantic
features. We use a deep learning network that takes in RGB point clouds and
extracts local features with five 3-D kernel point convolutional (KPConv)
layers. We specifically train the KPConv layers on the semantic segmentation
task to ensure that the extracted local features are semantically meaningful.
Then, feature maps from all the five KPConv layers are concatenated together
and fed into the NetVLAD layer to generate the global descriptors. The approach
is trained and evaluated using a large-scale indoor place recognition dataset
derived from the ScanNet dataset, with a test set comprising 3,608 point clouds
generated from 100 different rooms. Comparison with a traditional feature based
method and three state-of-the-art deep learning methods demonstrate that the
approach significantly outperforms all four methods, achieving, for example, a
top-3 average recall rate of 75% compared with 41% for the closest rival
method.

25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph