  Domain adaptation is an important task to enable learning when labels are
scarce. While most works focus only on the image modality, there are many
important multi-modal datasets. In order to leverage multi-modality for domain
adaptation, we propose cross-modal learning, where we enforce consistency
between the predictions of two modalities via mutual mimicking. We constrain
our network to make correct predictions on labeled data and consistent
predictions across modalities on unlabeled target-domain data. Experiments in
unsupervised and semi-supervised domain adaptation settings prove the
effectiveness of this novel domain adaptation strategy. Specifically, we
evaluate on the task of 3D semantic segmentation using the image and point
cloud modality. We leverage recent autonomous driving datasets to produce a
wide variety of domain adaptation scenarios including changes in scene layout,
lighting, sensor setup and weather, as well as the synthetic-to-real setup. Our
method significantly improves over previous uni-modal adaptation baselines on
all adaption scenarios. Code will be made available.
