Relationship Between Semantic Segmentation Model and Additional Features for 3D Point Clouds Obtained from on-Vehicle LIDAR

Hisato Hashimoto, Shuichi Enokida

2024

Abstract

The study delves into semantic segmentation’s role in recognizing regions within data, with a focus on images and 3D point clouds. While images from wide-angle cameras are prevalent, they falter in challenging environments like low light. In such cases, LIDAR (Laser Imaging Detection and Ranging), despite its lower resolution, excels. The combination of LIDAR and semantic segmentation proves effective for outdoor environment understanding. However, highly accurate models often demand substantial parameters, leading to computational challenges. Techniques like knowledge distillation and pruning offer solutions, though with possible accuracy trade-offs. This research introduces a strategy to merge feature descriptors, such as reflectance intensity and histograms, into the semantic segmentation model. This process balances accuracy and computational efficiency. The findings suggest that incorporating feature descriptors suits smaller models, while larger models can benefit from optimizing computation and utilizing feature descriptors for recognition tasks. Ultimately, this research contributes to the evolution of resource-efficient semantic segmentation models for autonomous driving and similar fields.

Paper Citation

in Harvard Style

Hashimoto H. and Enokida S. (2024). Relationship Between Semantic Segmentation Model and Additional Features for 3D Point Clouds Obtained from on-Vehicle LIDAR. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 547-553. DOI: 10.5220/0012374400003660

in Bibtex Style

@conference{visapp24,
author={Hisato Hashimoto and Shuichi Enokida},
title={Relationship Between Semantic Segmentation Model and Additional Features for 3D Point Clouds Obtained from on-Vehicle LIDAR},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2024},
pages={547-553},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012374400003660},
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 2: VISAPP
TI - Relationship Between Semantic Segmentation Model and Additional Features for 3D Point Clouds Obtained from on-Vehicle LIDAR
SN - 978-989-758-679-8
AU - Hashimoto H.
AU - Enokida S.
PY - 2024
SP - 547
EP - 553
DO - 10.5220/0012374400003660
PB - SciTePress