LiON: Learning Point-wise Abstaining Penalty for LiDAR Outlier DetectioN Using Diverse Synthetic Data

Shaocong Xu; Pengfei Li; Qianpu Sun; Xinyu Liu; Yang Li; Shihui Guo; Zhen Wang; Bo Jiang; Rui Wang; Kehua Sheng; Bo Zhang; Li Jiang; Hao Zhao; Yilun Chen

arXiv · 2309.10230

LiON: Learning Point-wise Abstaining Penalty for LiDAR Outlier DetectioN Using Diverse Synthetic Data

Shaocong Xu·Pengfei Li·Qianpu Sun·Xinyu Liu·Yang Li·Shihui Guo·Zhen Wang·Bo Jiang·Rui Wang·Kehua Sheng·Bo Zhang·Li Jiang·Hao Zhao·Yilun Chen

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Abstract

LiDAR-based semantic scene understanding is an important module in the modern autonomous driving perception stack. However, identifying outlier points in a LiDAR point cloud is challenging as LiDAR point clouds lack semantically-rich information. While former SOTA methods adopt heuristic architectures, we revisit this problem from the perspective of Selective Classification, which introduces a selective function into the standard closed-set classification setup. Our solution is built upon the basic idea of abstaining from choosing any inlier categories but learns a point-wise abstaining penalty with a margin-based loss. Apart from learning paradigms, synthesizing outliers to approximate unlimited real outliers is also critical, so we propose a strong synthesis pipeline that generates outliers originated from various factors: object categories, sampling patterns and sizes. We demonstrate that learning different abstaining penalties, apart from point-wise penalty, for different types of (synthesized) outliers can further improve the performance. We benchmark our method on SemanticKITTI and nuScenes and achieve SOTA results. Codes are available at https://github.com/Daniellli/LiON/.

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Shaocong Xu, Pengfei Li, Qianpu Sun, Xinyu Liu, Yang Li, Shihui Guo, Zhen Wang, Bo Jiang, Rui Wang, Kehua Sheng, Bo Zhang, Li Jiang, Hao Zhao, Yilun Chen. 2024-12-19. LiON: Learning Point-wise Abstaining Penalty for LiDAR Outlier DetectioN Using Diverse Synthetic Data. https://arxiv.org/abs/2309.10230

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