ClimaOoD: 通过物理真实合成数据提升异常分割
ClimaOoD: Improving Anomaly Segmentation via Physically Realistic Synthetic Data
AI总结：
ClimaOoD通过生成物理真实且语义连贯的合成数据，提升异常分割的鲁棒性和泛化能力。
摘要：
异常分割旨在检测和定位超出预定义语义类别的未知或分布外（OoD）对象，这是安全自动驾驶不可或缺的能力。然而，异常数据的稀缺性和多样性有限严重限制了模型在开放世界环境中的泛化能力。现有方法通过合成数据生成来缓解这一问题，要么通过将外部物体复制粘贴到驾驶场景中，要么通过利用文本到图像扩散模型来填充异常区域。尽管这些方法提高了异常多样性，但往往缺乏上下文连贯性和物理真实性，导致合成数据与真实数据之间存在领域差距。在本文中，我们提出了ClimaDrive，一种语义引导的图像到图像框架，用于生成语义连贯、天气多样且物理合理的OoD驾驶数据。ClimaDrive统一了结构引导的多天气生成与提示驱动的异常填充，使能够创建视觉逼真的训练数据。基于此框架，我们构建了ClimaOoD，一个覆盖六个代表性驾驶场景的大型基准，涵盖晴天和恶劣天气条件。在四种最先进的方法上进行了广泛实验，结果显示，使用ClimaOoD训练可显著提高异常分割的鲁棒性。在所有方法中，AUROC、AP和FPR95均显示出显著提升，FPR95从RbA在Fishyscapes LAF上的3.97降至3.52。这些结果表明，ClimaOoD增强了模型的鲁棒性，为开放世界异常检测提供了有价值的训练数据。
英文摘要：
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.
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