  Domain adaptation is critical for success when confronting with the lack of
annotations in a new domain. As the huge time consumption of labeling process
on 3D point cloud, domain adaptation for 3D semantic segmentation is of great
expectation. With the rise of multi-modal datasets, large amount of 2D images
are accessible besides 3D point clouds. In light of this, we propose to further
leverage 2D data for 3D domain adaptation by intra and inter domain cross modal
learning. As for intra-domain cross modal learning, most existing works sample
the dense 2D pixel-wise features into the same size with sparse 3D point-wise
features, resulting in the abandon of numerous useful 2D features. To address
this problem, we propose Dynamic sparse-to-dense Cross Modal Learning (DsCML)
to increase the sufficiency of multi-modality information interaction for
domain adaptation. For inter-domain cross modal learning, we further advance
Cross Modal Adversarial Learning (CMAL) on 2D and 3D data which contains
different semantic content aiming to promote high-level modal complementarity.
We evaluate our model under various multi-modality domain adaptation settings
including day-to-night, country-to-country and dataset-to-dataset, brings large
improvements over both uni-modal and multi-modal domain adaptation methods on
all settings.
