Abstract
Anomaly segmentation seeks to detect and localize unknown or out-of-distribution (OoD) objects that fall outside predefined semantic classes a capability essential for safe autonomous driving. However, the scarcity and limited diversity of anomaly data severely constrain model generalization in open-world environments. Existing approaches mitigate this issue through synthetic data generation, either by copy-pasting external objects into driving scenes or by leveraging text-to-image diffusion models to inpaint anomalous regions. While these methods improve anomaly diversity, they often lack contextual coherence and physical realism, resulting in domain gaps between synthetic and real data. In this paper, we present ClimaDrive, a semantics-guided image-to-image framework for synthesizing semantically coherent, weather-diverse, and physically plausible OoD driving data. ClimaDrive unifies structure-guided multi-weather generation with prompt-driven anomaly inpainting, enabling the creation of visually realistic training data. Based on this framework, we construct ClimaOoD, a large-scale benchmark spanning six representative driving scenarios under both clear and adverse weather conditions. Extensive experiments on four state-of-the-art methods show that training with ClimaOoD leads to robust improvements in anomaly segmentation. Across all methods, AUROC, AP, and FPR95 show notable gains, with FPR95 dropping from 3.97 to 3.52 for RbA on Fishyscapes LAF. These results demonstrate that ClimaOoD enhances model robustness, offering valuable training data for better generalization in open-world anomaly detection.
Abstract (translated)
异常分割旨在检测和定位未知或分布外（OoD）的物体，这些物体超出了预定义的语义类别范畴，这一能力对于安全自主驾驶至关重要。然而，缺乏多样性以及稀少性的异常数据严重制约了模型在开放环境中的泛化能力。现有方法通过生成合成数据来缓解此问题，例如将外部对象复制粘贴到驾驶场景中或将文本转换为图像扩散模型用于修补异常区域。尽管这些方法提高了异常多样性和训练的便利性，但它们通常缺乏上下文连贯性和物理现实感，导致合成数据与真实数据之间的领域差距。 在本文中，我们介绍了ClimaDrive，这是一种基于语义指导的图像到图像框架，专门用于生成语义上一致、天气多样化且物理上合理的OoD驾驶数据。ClimaDrive结合了结构引导下的多天气生成和提示驱动的异常修补，从而能够创建视觉上真实的训练数据。在此基础上，我们构建了ClimaOoD，这是一个大型基准测试库，在六种具有代表性的驾驶场景下进行测试，涵盖了晴朗及恶劣天气条件。 对四种最先进的方法进行了广泛的实验后显示，使用ClimaOoD进行训练可以显著提高异常分割的鲁棒性。所有方法在AUROC、AP和FPR95指标上都取得了显著的进步，在Fishyscapes LAF数据集上RbA模型的FPR95从3.97降低到3.52。 这些结果表明，ClimaOoD可以增强模型的鲁棒性，并提供有价值的训练数据以提高开放世界异常检测中的泛化能力。
URL
https://arxiv.org/abs/2512.02686
