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

TL;DR

This paper introduces LiON, a novel approach for LiDAR outlier detection in autonomous driving, using learned point-wise abstaining penalties and diverse synthetic data to improve outlier identification accuracy.

Contribution

LiON proposes a point-wise abstaining penalty learning framework combined with a synthetic outlier generation pipeline, advancing LiDAR outlier detection performance.

Findings

Achieves state-of-the-art results on SemanticKITTI and nuScenes datasets.

Effective outlier detection by learning abstaining penalties for different outlier types.

Synthetic data generation enhances outlier detection robustness.

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/.