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Authors: Richard Marcus 1 ; Niklas Knoop 2 ; Bernhard Egger 1 and Marc Stamminger 1

Affiliations: 1 Chair of Visual Computing, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany ; 2 Elektronische Fahrwerksysteme GmbH, Germany

Keyword(s): Autonomous Driving, LiDAR, General Adversarial Neural Network, Image-to-image Translation.

Abstract: Virtual testing is a crucial task to ensure safety in autonomous driving, and sensor simulation is an important task in this domain. Most current LiDAR simulations are very simplistic and are mainly used to perform initial tests, while the majority of insights are gathered on the road. In this paper, we propose a lightweight approach for more realistic LiDAR simulation that learns a real sensor’s behavior from test drive data and transforms this to the virtual domain. The central idea is to cast the simulation into an image-to-image translation problem. We train our pix2pix based architecture on two real world data sets, namely the popular KITTI data set and the Audi Autonomous Driving Dataset which provide both, RGB and LiDAR images. We apply this network on synthetic renderings and show that it generalizes sufficiently from real images to simulated images. This strategy enables to skip the sensor-specific, expensive and complex LiDAR physics simulation in our synthetic world and av oids oversimplification and a large domain-gap through the clean synthetic environment. (More)

CC BY-NC-ND 4.0

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Paper citation in several formats:
Marcus, R.; Knoop, N.; Egger, B. and Stamminger, M. (2022). A Lightweight Machine Learning Pipeline for LiDAR-simulation. In Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - DeLTA, ISBN 978-989-758-584-5; ISSN 2184-9277, pages 176-183. DOI: 10.5220/0011309100003277

@conference{delta22,
author={Richard Marcus. and Niklas Knoop. and Bernhard Egger. and Marc Stamminger.},
title={A Lightweight Machine Learning Pipeline for LiDAR-simulation},
booktitle={Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - DeLTA,},
year={2022},
pages={176-183},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011309100003277},
isbn={978-989-758-584-5},
issn={2184-9277},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - DeLTA,
TI - A Lightweight Machine Learning Pipeline for LiDAR-simulation
SN - 978-989-758-584-5
IS - 2184-9277
AU - Marcus, R.
AU - Knoop, N.
AU - Egger, B.
AU - Stamminger, M.
PY - 2022
SP - 176
EP - 183
DO - 10.5220/0011309100003277