Toward Physics-Aware Deep Learning Architectures for LiDAR Intensity Simulation

Vivek Anand, Bharat Lohani, Gaurav Pandey, Rakesh Mishra

2024

Abstract

Autonomous vehicles (AVs) heavily rely on LiDAR perception for environment understanding and navigation. LiDAR intensity provides valuable information about the reflected laser signals and plays a crucial role in enhancing the perception capabilities of AVs. However, accurately simulating LiDAR intensity remains a challenge due to the unavailability of material properties of the objects in the environment, and complex interactions between the laser beam and the environment. The proposed method aims to improve the accuracy of intensity simulation by incorporating physics-based modalities within the deep learning framework. One of the key entities that captures the interaction between the laser beam and the objects is the angle of incidence. In this work, we demonstrate that adding the LiDAR incidence angle as a separate input modality to the deep neural networks significantly enhances the results. We integrated this novel input modality into two prominent deep learning architectures: U-NET, a Convolutional Neural Network (CNN), and Pix2Pix, a Generative Adversarial Network (GAN). We investigated these two architectures for the intensity prediction task and used SemanticKITTI and VoxelScape datasets for experiments. The comprehensive analysis reveals that both architectures benefit from the incidence angle as an additional input. Moreover, the Pix2Pix architecture outperforms U-NET, especially when the incidence angle is incorporated.

Paper Citation

in Harvard Style

Anand V., Lohani B., Pandey G. and Mishra R. (2024). Toward Physics-Aware Deep Learning Architectures for LiDAR Intensity Simulation. In Proceedings of the 14th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH; ISBN 978-989-758-708-5, SciTePress, pages 47-56. DOI: 10.5220/0012741500003758

in Bibtex Style

@conference{simultech24,
author={Vivek Anand and Bharat Lohani and Gaurav Pandey and Rakesh Mishra},
title={Toward Physics-Aware Deep Learning Architectures for LiDAR Intensity Simulation},
booktitle={Proceedings of the 14th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH},
year={2024},
pages={47-56},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012741500003758},
isbn={978-989-758-708-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH
TI - Toward Physics-Aware Deep Learning Architectures for LiDAR Intensity Simulation
SN - 978-989-758-708-5
AU - Anand V.
AU - Lohani B.
AU - Pandey G.
AU - Mishra R.
PY - 2024
SP - 47
EP - 56
DO - 10.5220/0012741500003758
PB - SciTePress