  3D semantic segmentation is a critical task in many real-world applications,
such as autonomous driving, robotics, and mixed reality. However, the task is
extremely challenging due to ambiguities coming from the unstructured, sparse,
and uncolored nature of the 3D point clouds. A possible solution is to combine
the 3D information with others coming from sensors featuring a different
modality, such as RGB cameras. Recent multi-modal 3D semantic segmentation
networks exploit these modalities relying on two branches that process the 2D
and 3D information independently, striving to maintain the strength of each
modality. In this work, we first explain why this design choice is effective
and then show how it can be improved to make the multi-modal semantic
segmentation more robust to domain shift. Our surprisingly simple contribution
achieves state-of-the-art performances on four popular multi-modal unsupervised
domain adaptation benchmarks, as well as better results in a domain
generalization scenario.
