Analysis of Point Cloud Domain Gap Effects for 3D Object Detection Evaluation

Aitor Iglesias, Aitor Iglesias, Mikel García, Mikel García, Nerea Aranjuelo, Ignacio Arganda-Carreras, Ignacio Arganda-Carreras, Ignacio Arganda-Carreras, Ignacio Arganda-Carreras, Marcos Nieto

2024

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 domain 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.

Paper Citation

in Harvard Style

Iglesias A., García M., Aranjuelo N., Arganda-Carreras I. and Nieto M. (2024). Analysis of Point Cloud Domain Gap Effects for 3D Object Detection Evaluation. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 278-285. DOI: 10.5220/0012357200003660

in Bibtex Style

@conference{visapp24,
author={Aitor Iglesias and Mikel García and Nerea Aranjuelo and Ignacio Arganda-Carreras and Marcos Nieto},
title={Analysis of Point Cloud Domain Gap Effects for 3D Object Detection Evaluation},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP},
year={2024},
pages={278-285},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012357200003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP
TI - Analysis of Point Cloud Domain Gap Effects for 3D Object Detection Evaluation
SN - 978-989-758-679-8
AU - Iglesias A.
AU - García M.
AU - Aranjuelo N.
AU - Arganda-Carreras I.
AU - Nieto M.
PY - 2024
SP - 278
EP - 285
DO - 10.5220/0012357200003660
PB - SciTePress