xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation

Understand

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.