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.

翻译：摘要：在单一标注数据集上训练的模型难以泛化到另一领域，因为数据域之间可能存在多种偏移。激光雷达数据尤为如此，由于激光雷达模式差异或采集条件变化，模型性能可能出现显著差异。本文针对语义分割任务中的无监督域适应问题展开研究。为缓解该问题，我们提出一种无监督辅助任务，在源域和目标域数据上同时学习隐式底层表面表征。由于两域共享同一潜在表征，模型被迫适应两数据源之间的差异。这一新策略不同于经典的统计散度最小化方法或激光雷达专用域适应技术。实验表明，在真实到真实与合成到真实场景中，我们的方法均取得了优于当前最佳技术的性能。