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This paper addresses the problem of 3D LiDAR anomaly segmentation by learning to identify out-of-distribution objects directly in the feature space. They model the feature distribution of inlier classes to constrain anomalous samples, avoiding reliance on 2D post-processing. To address limitations in existing datasets, they introduce a new mixed real-synthetic dataset with more complex scenarios and anomalies, demonstrating state-of-the-art performance on existing datasets and competitive results on their new dataset.
Current 3D anomaly detection struggles with real-world complexity, but this new approach directly models inlier feature distributions, achieving state-of-the-art results and offering a more robust solution.
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 exhibits a severe domain gap due to its sensor resolution. To bridge this gap, we introduce a set of mixed real-synthetic datasets for 3D LiDAR anomaly segmentation, built upon established semantic segmentation benchmarks, with multiple out-of-distribution objects and diverse, complex environments. Extensive experiments demonstrate that our approach achieves state-of-the-art and competitive results on the existing real-world dataset and the newly introduced mixed datasets, respectively, validating the effectiveness of our method and the utility of the proposed datasets. Code and datasets are available at https://simom0.github.io/lido-page/.
