通过特征分布建模，实现3D LiDAR异常分割的高效识别。
Learning to Identify Out-of-Distribution Objects for 3D LiDAR Anomaly Segmentation
- 在特征空间直接建模正常类别分布，约束异常样本。
- 在真实数据集上达最优，在混合新数据集上表现稳健。
- 适合自动驾驶与机器人感知中异常检测任务。
理解周围环境是自动驾驶与机器人感知的基础。区分已知类别与未见物体对真实场景至关重要，这正是异常分割的目标。然而，3D领域的研究仍有限，多数方法沿用2D视觉的后处理技术。为此，我们提出一种新方法，直接在特征空间操作，通过建模正常类别的特征分布来约束异常样本。此外，现有公开的3D LiDAR异常分割数据集仅含简单场景、少量异常实例，且因传感器分辨率导致严重域偏移。为弥补这一不足，我们构建了基于经典语义分割基准的混合真实-合成数据集，包含多种分布外物体和复杂多变环境。大量实验表明，我们的方法在现有真实数据集上达到最先进水平，在新引入的混合数据集上也表现优异，验证了方法有效性与数据集实用性。代码与数据集详见https://simom0.github.io/lido-page/。
原文摘要 · Abstract (English)
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/.
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