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

arXiv: https://arxiv.org/abs/2309.10230 · HTML: https://mlsanity.com/paper/2309.10230/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2023.

## Built on by (most cited first, 3 of 3)

- [GSRender: Deduplicated Occupancy Prediction via Weakly Supervised 3D Gaussian Splatting](https://mlsanity.com/paper/2412.14579/) (2024)
- [Relative Energy Learning for LiDAR Out-of-Distribution Detection](https://mlsanity.com/paper/2511.06720/) (2025)
- [Neural Distribution Prior for LiDAR Out-of-Distribution Detection](https://mlsanity.com/paper/2604.09232/) (2026)

## Abstract (arXiv)

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/.