Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Vision-based Large-scale 3D Semantic Mapping for Autonomous Driving
  Applications   In this paper, we present a complete pipeline for 3D semantic mapping solely
based on a stereo camera system. The pipeline comprises a direct sparse visual
odometry front-end as well as a back-end for global optimization including GNSS
integration, and semantic 3D point cloud labeling. We propose a simple but
effective temporal voting scheme which improves the quality and consistency of
the 3D point labels. Qualitative and quantitative evaluations of our pipeline
are performed on the KITTI-360 dataset. The results show the effectiveness of
our proposed voting scheme and the capability of our pipeline for efficient
large-scale 3D semantic mapping. The large-scale mapping capabilities of our
pipeline is furthermore demonstrated by presenting a very large-scale semantic
map covering 8000 km of roads generated from data collected by a fleet of
vehicles.

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The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
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The categories of this paper are: q-fin
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The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio