学习识别分布外目标以用于3D激光雷达异常分割
Learning to Identify Out-of-Distribution Objects for 3D LiDAR Anomaly Segmentation
在激光雷达特征空间中直接建模内点类分布以约束异常样本，可替代从2D视觉迁移的后处理式异常打分；同时提供的混合真实-合成激光雷达异常分割数据集可用于评测3D开放世界感知的OOD检测能力。
原文依据 we propose a new efficient approach that directly operates in the feature space, modeling the feature distribution of inlier classes to constrain anomalous samples
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论理解周围环境是自动驾驶与机器人感知的基础。在真实环境中，区分已知类别与先前未见过的目标至关重要，这正是异常分割所处理的问题。然而，3D领域的研究仍然有限，现有方法大多沿用2D视觉中的后处理技术。为弥补这一空白，我们提出一种新的高效方法，直接在特征空间中操作，对内点类别的特征分布进行建模以约束异常样本。此外，目前唯一公开的3D激光雷达异常分割数据集仅包含简单场景，异常实例稀少，且因其传感器分辨率而存在严重的域间差距。为弥合这一差距，我们基于已有的语义分割基准，引入一组用于3D激光雷达异常分割的混合真实-合成数据集，其中包含多种分布外目标以及多样而复杂的环境。大量实验表明，我们的方法在现有真实世界数据集和新引入的混合数据集上分别取得了最优结果和具有竞争力的结果，验证了方法的有效性以及所提出数据集的实用价值。代码与数据集见 https://simom0.github.io/lido-page/。
英文原文摘要 1343 字符
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/.