  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.
