A Lightweight Machine Learning Pipeline for LiDAR-simulation

Richard Marcus; Niklas Knoop; Bernhard Egger; Marc Stamminger

Research.Publish.Connect.

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Paper

A Lightweight Machine Learning Pipeline for LiDAR-simulation

Topics: Convolutional Neural Networks (CNN); Generative Adversarial Networks (GAN); Image Classification

In Proceedings of the 3rd International Conference on Deep Learning Theory and Applications DeLTA - Volume 1, 176-183, 2022 , Lisbon, Portugal

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

Guest: Register as new SciTePress user now for free.

SciTePress user: please login.

My Papers

You are not signed in, therefore limits apply to your IP address 18.232.179.37

In the current month:

Recent papers: 100 available of 100 total

2⁺ years older papers: 200 available of 200 total

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, SciTePress, 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
PB - SciTePress