Deep-learning models for 3D point cloud semantic segmentation exhibit limited
generalization capabilities when trained and tested on data captured with
different sensors or in varying environments due to domain shift. Domain
adaptation methods can be employed to mitigate this domain shift, for instance,
by simulating sensor noise, developing domain-agnostic generators, or training
point cloud completion networks. Often, these methods are tailored for range
view maps or necessitate multi-modal input. In contrast, domain adaptation in
the image domain can be executed through sample mixing, which emphasizes input
data manipulation rather than employing distinct adaptation modules. In this
study, we introduce compositional semantic mixing for point cloud domain
adaptation, representing the first unsupervised domain adaptation technique for
point cloud segmentation based on semantic and geometric sample mixing. We
present a two-branch symmetric network architecture capable of concurrently
processing point clouds from a source domain (e.g. synthetic) and point clouds
from a target domain (e.g. real-world). Each branch operates within one domain
by integrating selected data fragments from the other domain and utilizing
semantic information derived from source labels and target (pseudo) labels.
Additionally, our method can leverage a limited number of human point-level
annotations (semi-supervised) to further enhance performance. We assess our
approach in both synthetic-to-real and real-to-real scenarios using LiDAR
datasets and demonstrate that it significantly outperforms state-of-the-art
methods in both unsupervised and semi-supervised settings.Comment: TPAMI. arXiv admin note: text overlap with arXiv:2207.0977