Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for
Challenging Tasks in 3D Mapping
- URL: http://arxiv.org/abs/2111.11348v1
- Date: Mon, 22 Nov 2021 16:54:28 GMT
- Title: Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for
Challenging Tasks in 3D Mapping
- Authors: Jean-Emmanuel Deschaud and David Duque and Jean Pierre Richa and
Santiago Velasco-Forero and Beatriz Marcotegui and and Fran\c{c}ois Goulette
- Abstract summary: Paris-CARLA-3D is a dataset of several dense colored point clouds of outdoor environments built by a mobile LiDAR and camera system.
The data are composed of two sets with synthetic data from the open source CARLA simulator and real data acquired in the city of Paris.
- Score: 6.573006160924016
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Paris-CARLA-3D is a dataset of several dense colored point clouds of outdoor
environments built by a mobile LiDAR and camera system. The data are composed
of two sets with synthetic data from the open source CARLA simulator (700
million points) and real data acquired in the city of Paris (60 million
points), hence the name Paris-CARLA-3D. One of the advantages of this dataset
is to have simulated the same LiDAR and camera platform in the open source
CARLA simulator as the one used to produce the real data. In addition, manual
annotation of the classes using the semantic tags of CARLA was performed on the
real data, allowing the testing of transfer methods from the synthetic to the
real data. The objective of this dataset is to provide a challenging dataset to
evaluate and improve methods on difficult vision tasks for the 3D mapping of
outdoor environments: semantic segmentation, instance segmentation, and scene
completion. For each task, we describe the evaluation protocol as well as the
experiments carried out to establish a baseline.
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