3D semantic segmentation under adverse weather conditions

Tong Liu; Yunhan Lin

doi:10.1117/12.3012696

7 March 2024 3D semantic segmentation under adverse weather conditions

Tong Liu, Yunhan Lin

Proceedings Volume 13086, MIPPR 2023: Pattern Recognition and Computer Vision; 130860W (2024) https://doi.org/10.1117/12.3012696
Event: Twelfth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2023), 2023, Wuhan, China

Abstract

The accurate 3D comprehension of point cloud scenes in diverse weather conditions holds paramount significance in various applications such as autonomous driving in contemporary automobiles, outdoor operations of robots, and autonomous drones. Presently, the majority of studies on semantic segmentation algorithms for 3D point clouds primarily focus on clear weather conditions. However, adverse weather conditions introduce specific types of noise that significantly deteriorate the quality of point clouds. Consequently, this poses a challenge in achieving high accuracy and efficiency in point cloud semantic segmentation for outdoor large-scale scenarios. To tackle this issue, this paper presents a novel semantic segmentation method designed for large scenes encompassing point cloud and foggy weather conditions. We further validate our approach using the Foggy Semantickitti dataset, thereby effectively enhancing the average cross-parallel ratio while maintaining computational efficiency.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Tong Liu and Yunhan Lin "3D semantic segmentation under adverse weather conditions", Proc. SPIE 13086, MIPPR 2023: Pattern Recognition and Computer Vision, 130860W (7 March 2024); https://doi.org/10.1117/12.3012696

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KEYWORDS

Point clouds

Semantics

Fiber optic gyroscopes

Adverse weather

Data modeling

LIDAR

Denoising

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