  Unsupervised Domain Adaptation (UDA) is crucial to tackle the lack of
annotations in a new domain. There are many multi-modal datasets, but most UDA
approaches are uni-modal. In this work, we explore how to learn from
multi-modality and propose cross-modal UDA (xMUDA) where we assume the presence
of 2D images and 3D point clouds for 3D semantic segmentation. This is
challenging as the two input spaces are heterogeneous and can be impacted
differently by domain shift. In xMUDA, modalities learn from each other through
mutual mimicking, disentangled from the segmentation objective, to prevent the
stronger modality from adopting false predictions from the weaker one. We
evaluate on new UDA scenarios including day-to-night, country-to-country and
dataset-to-dataset, leveraging recent autonomous driving datasets. xMUDA brings
large improvements over uni-modal UDA on all tested scenarios, and is
complementary to state-of-the-art UDA techniques. Code is available at
https://github.com/valeoai/xmuda.
