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Deep-learning models for 3D point cloud semantic segmentation exhibit limitedgeneralization capabilities when trained and tested on data captured withdifferent sensors or in varying environments due to domain shift. Domainadaptation methods can be employed to mitigate this domain shift, for instance,by simulating sensor noise, developing domain-agnostic generators, or trainingpoint cloud completion networks. Often, these methods are tailored for rangeview maps or necessitate multi-modal input. In contrast, domain adaptation inthe image domain can be executed through sample mixing, which emphasizes inputdata manipulation rather than employing distinct adaptation modules. In thisstudy, we introduce compositional semantic mixing for point cloud domainadaptation, representing the first unsupervised domain adaptation technique forpoint cloud segmentation based on semantic and geometric sample mixing. Wepresent a two-branch symmetric network architecture capable of concurrentlyprocessing point clouds from a source domain (e.g. synthetic) and point cloudsfrom a target domain (e.g. real-world). Each branch operates within one domainby integrating selected data fragments from the other domain and utilizingsemantic information derived from source labels and target (pseudo) labels.Additionally, our method can leverage a limited number of human point-levelannotations (semi-supervised) to further enhance performance. We assess ourapproach in both synthetic-to-real and real-to-real scenarios using LiDARdatasets and demonstrate that it significantly outperforms state-of-the-artmethods in both unsupervised and semi-supervised settings.

Compositional Semantic Mix for Domain Adaptation in Point Cloud Segmentation