标题： 三维应用中的分布外检测：综述 显示英文标题

标题： Out-of-distribution detection in 3D applications: a review

摘要： 检测在训练集中不常见的物体的能力是许多3D应用中的关键能力，包括自动驾驶。 用于物体识别的机器学习方法通常假设在推理过程中遇到的所有物体类别都属于训练数据中存在的封闭类别集。 这种假设限制了在现实世界中的泛化能力，因为训练过程中未见过的物体可能会被错误分类或完全忽略。 作为可靠AI的一部分，OOD检测可以识别与训练分布显著偏离的输入。 本文提供了在可信和不确定AI更广泛范围内的OOD检测的全面概述。 我们首先介绍跨不同领域的关键应用场景，介绍涵盖多种模态的基准数据集，并讨论评估指标。 接下来，我们对OOD检测方法进行比较分析，探讨模型结构、不确定性指标和分布距离分类法，以及不确定性校准技术。 最后，我们强调有前景的研究方向，包括对抗鲁棒的OOD检测和故障识别，这些特别适用于3D应用。 本文为OOD检测提供了理论和实践见解，展示了诸如3D视觉集成等新兴研究机会。 这些见解有助于新研究人员更有效地探索该领域，从而促进可靠、安全和强大的AI系统的发展。 显示英文摘要

摘要： The ability to detect objects that are not prevalent in the training set is a critical capability in many 3D applications, including autonomous driving. Machine learning methods for object recognition often assume that all object categories encountered during inference belong to a closed set of classes present in the training data. This assumption limits generalization to the real world, as objects not seen during training may be misclassified or entirely ignored. As part of reliable AI, OOD detection identifies inputs that deviate significantly from the training distribution. This paper provides a comprehensive overview of OOD detection within the broader scope of trustworthy and uncertain AI. We begin with key use cases across diverse domains, introduce benchmark datasets spanning multiple modalities, and discuss evaluation metrics. Next, we present a comparative analysis of OOD detection methodologies, exploring model structures, uncertainty indicators, and distributional distance taxonomies, alongside uncertainty calibration techniques. Finally, we highlight promising research directions, including adversarially robust OOD detection and failure identification, particularly relevant to 3D applications. The paper offers both theoretical and practical insights into OOD detection, showcasing emerging research opportunities such as 3D vision integration. These insights help new researchers navigate the field more effectively, contributing to the development of reliable, safe, and robust AI systems.