Learning to Identify Out-of-Distribution Objects for 3D LiDAR Anomaly Segmentation
Understanding the surrounding environment is fundamental in autonomous driving and robotic perception. Distinguishing between known classes and previously unseen objects is crucial in real-world environments, as done in Anomaly Segmentation. However, research in the 3D field remains limited, with most existing approaches applying post-processing techniques from 2D vision. To cover this lack, we propose a new efficient approach that directly operates in the feature space, modeling the feature distribution of inlier classes to constrain anomalous samples. Moreover, the only publicly available 3D LiDAR anomaly segmentation dataset contains simple scenarios, with few anomaly instances, and ex...
| Authors | Simone Mosco, Daniel Fusaro, Alberto Pretto |
|---|---|
| Primary category | cs.CV |
| Published | 26 April 2026 |
| Last updated at source | 26 April 2026 |
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