SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation

Understand

Learning models on one labeled dataset that generalize well on another domain is a difficult task, as several shifts might happen between the data domains.

• This is notably the case for lidar data, for which models can exhibit large performance discrepancies due for instance to different lidar patterns or changes in acquisition conditions.
• This paper addresses the corresponding Unsupervised Domain Adaptation (UDA) task for semantic segmentation.
• To mitigate this problem, we introduce an unsupervised auxiliary task of learning an implicit underlying surface representation simultaneously on source and target data.