Authors:
Aitor Iglesias
1
;
2
;
Mikel García
1
;
2
;
Nerea Aranjuelo
1
;
Ignacio Arganda-Carreras
2
;
3
;
4
;
5
and
Marcos Nieto
1
Affiliations:
1
Fundación Vicomtech, Connected and Cooperative Automated Systems, Spain
;
2
University of the Basque Country (UPV/EHU), Donostia - San Sebastian, Spain
;
3
IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
;
4
Donostia International Physics Center (DIPC), Donostia - San Sebastian, Spain
;
5
Biofisika Institute, Leioa, Spain
Keyword(s):
Point Clouds, Deep Learning, Domain Gap, Object Detection, Simulation.
Abstract:
The development of autonomous driving systems heavily relies on high-quality LiDAR data, which is essential for robust object detection and scene understanding. Nevertheless, obtaining a substantial amount of such data for effective training and evaluation of autonomous driving algorithms is a major challenge. To overcome this limitation, recent studies are taking advantage of advancements in realistic simulation engines, such as CARLA, which have provided a breakthrough in generating synthetic LiDAR data that closely resembles real-world scenarios. However, these data are far from being identical to real data. In this study, we address the domain gap between real LiDAR data and synthetic data. We train deep-learning models for object detection using real data. Then, those models are rigorously evaluated using synthetic data generated in CARLA. By quantifying the discrepancies between the model’s performance on real and synthetic data, the present study shows that there is indeed a d
omain gap between the two types of data and does not affect equal to different model architectures. Finally, we propose a method for synthetic data processing to reduce this domain gap. This research contributes to enhancing the use of synthetic data for autonomous driving systems.
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