Fast, trusted semantic segmentation of LiDAR point clouds with evidential fusion

Feng Zhang; Jinglong Wang; Wenyi Fan; Shengchao Guo; Lei Chen

doi:10.1117/12.3057902

13 February 2025 Fast, trusted semantic segmentation of LiDAR point clouds with evidential fusion

Feng Zhang, Jinglong Wang, Wenyi Fan, Shengchao Guo, Lei Chen

Author Affiliations +

Proceedings Volume 13539, Sixteenth International Conference on Graphics and Image Processing (ICGIP 2024); 135390M (2025) https://doi.org/10.1117/12.3057902
Event: Sixteenth International Conference on Graphics and Image Processing (ICGIP 2024), 2024, Nanjing, China

Abstract

The 3D point cloud is a fundamental data format and a vital information carrier for analyzing and understanding real-world scenes, particularly crucial for applications like autonomous driving. Current research work mainly focuses on how to improve model performance. However, accurately assessing predictive uncertainty remains crucial before models can achieve perfect precision, especially in risk-sensitive scenarios. We apply evidential deep learning to the field of point cloud semantic segmentation for the first time and propose a new uncertainty-aware segmentation head that can predict cognitive uncertainty and arbitrary uncertainty in a single forward reasoning. Furthermore, this method can be seamlessly integrated with any point cloud classification and segmentation model. In addition, by introducing arbitrary uncertainty into the loss function, overfitting to noise points is avoided to a certain extent. Experimental results confirm that this approach leads in inference speed and precision of uncertainty estimation, setting a new standard in the field.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Feng Zhang, Jinglong Wang, Wenyi Fan, Shengchao Guo, and Lei Chen "Fast, trusted semantic segmentation of LiDAR point clouds with evidential fusion", Proc. SPIE 13539, Sixteenth International Conference on Graphics and Image Processing (ICGIP 2024), 135390M (13 February 2025); https://doi.org/10.1117/12.3057902

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