Authors:
Iñigo Alonso
1
;
Luis Riazuelo
1
;
Luis Montesano
2
;
1
and
Ana C. Murillo
1
Affiliations:
1
RoPeRt Group, DIIS - I3A, Universidad de Zaragoza, Spain
;
2
Bitbrain, Zaragoza, Spain
Keyword(s):
Robotics, Autonomous Systems, LiDAR, Deep Learning, Semantic Segmentation, Domain Adaptation.
Abstract:
LiDAR semantic segmentation provides 3D semantic information about the environment, an essential cue for intelligent systems, such as autonomous vehicles, during their decision making processes. Unfortunately, the annotation process for this task is very expensive. To overcome this, it is key to find models that generalize well or adapt to additional domains where labeled data is limited. This work addresses the problem of unsupervised domain adaptation for LiDAR semantic segmentation models. We propose simple but effective strategies to reduce the domain shift by aligning the data distribution on the input space. Besides, we present a learning-based module to align the distribution of the semantic classes of the target domain to the source domain. Our approach achieves new state-of-the-art results on three different public datasets, which showcase adaptation to three different domains.