  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. As both domains share the same latent representation, the model is
forced to accommodate discrepancies between the two sources of data. This novel
strategy differs from classical minimization of statistical divergences or
lidar-specific domain adaptation techniques. Our experiments demonstrate that
our method achieves a better performance than the current state of the art,
both in real-to-real and synthetic-to-real scenarios.
