UniMix：迈向恶劣天气下具有域适应性和域泛化能力的LiDAR语义分割
UniMix: Towards Domain Adaptive and Generalizable LiDAR Semantic Segmentation in Adverse Weather
AI总结：
针对恶劣天气下LiDAR语义分割的域适应与泛化问题，提出UniMix，通过物理天气模拟、桥接域和通用混合算子增强模型鲁棒性，并在多项任务上超越现有方法。
摘要：
LiDAR语义分割（LSS）是自动驾驶中的一项关键任务，并已取得令人瞩目的进展。然而，以往的LSS方法通常是在晴朗天气下同一域内的数据集上进行研究和评估的。LSS模型在未见场景和各种天气条件下的鲁棒性，对于确保实际应用中的安全性和可靠性至关重要。为此，我们提出了UniMix，这是一种增强LSS模型适应性和泛化能力的通用方法。UniMix首先利用物理有效的恶劣天气模拟来构建一个桥接域（Bridge Domain），用于弥合晴朗天气场景与恶劣天气场景之间的域差距。随后，针对空间、强度和语义分布定义了一个通用混合算子（Universal Mixing operator），以利用来自给定域的混合样本创建中间域。通过将所提出的两种技术集成到师生框架中，UniMix有效缓解了域差距，并使LSS模型能够学习对天气鲁棒且域不变的表示。我们将UniMix用于两种主要设置：1）无监督域适应，即将模型从晴朗天气源域适应到恶劣天气目标域；2）域泛化，即学习一个能够很好泛化到恶劣天气下未见场景的模型。大量实验验证了UniMix在不同任务和数据集上的有效性，均取得了优于最先进方法的性能。代码将会发布。
英文摘要：
LiDAR semantic segmentation (LSS) is a critical task in autonomous driving and has achieved promising progress. However, prior LSS methods are conventionally investigated and evaluated on datasets within the same domain in clear weather. The robustness of LSS models in unseen scenes and all weather conditions is crucial for ensuring safety and reliability in real applications. To this end, we propose UniMix, a universal method that enhances the adaptability and generalizability of LSS models. UniMix first leverages physically valid adverse weather simulation to construct a Bridge Domain, which serves to bridge the domain gap between the clear weather scenes and the adverse weather scenes. Then, a Universal Mixing operator is defined regarding spatial, intensity, and semantic distributions to create the intermediate domain with mixed samples from given domains. Integrating the proposed two techniques into a teacher-student framework, UniMix efficiently mitigates the domain gap and enables LSS models to learn weather-robust and domain-invariant representations. We devote UniMix to two main setups: 1) unsupervised domain adaption, adapting the model from the clear weather source domain to the adverse weather target domain; 2) domain generalization, learning a model that generalizes well to unseen scenes in adverse weather. Extensive experiments validate the effectiveness of UniMix across different tasks and datasets, all achieving superior performance over state-of-the-art methods. The code will be released.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
