学习识别分布外对象以实现3D激光雷达异常分割

摘要

理解周围环境是自动驾驶与机器人感知的基础。在实际环境中，区分已知类别与未知物体至关重要，这正是异常分割的研究范畴。然而当前三维领域的研究仍显不足，现有方法大多直接套用二维视觉的后处理技术。为弥补这一空白，我们提出了一种直接在特征空间操作的高效方法，通过对正常类别的特征分布进行建模来约束异常样本。此外，目前唯一公开的三维激光雷达异常分割数据集仅包含简单场景和少量异常实例，且因传感器分辨率差异存在严重域差距。为消除这一差距，我们基于成熟的分割基准数据集构建了一套混合现实-虚拟的三维激光雷达异常分割数据集，其中包含多种分布外目标及复杂多变的环境。大量实验表明，我们的方法在现有真实数据集上达到最优性能，在新提出的混合数据集上取得竞争性结果，验证了方法的有效性与数据集的实用性。代码与数据集详见https://simom0.github.io/lido-page/。

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/.