Cross-Lidar Domain Adaptation for Semantic Segmentation via Semi-Supervised Learning and Map Annotation
This paper proposes a domain adaptation method for different lidars in point cloud semantic segmentation using semi-supervised learning and map annotation. This approach addresses the significant costs associated with acquiring point-level annotations in target domains. We first perform representation learning using large-scale unlabeled data. Subsequently, we conduct classification learning using a point cloud map integrated via coordinate transformations, eliminating the need to annotate individual frames. Experiments on our original TC dataset demonstrate that our method effectively bridges the domain gap from the SemanticKITTI dataset, improving the mean Intersection over Union (mIoU) by 48.6 points over a zero-shot baseline. Furthermore, our approach outperforms a strong baseline trained from scratch on the target domain by 7.2 points in mIoU. These results establish a cost-efficient solution for deploying diverse sensors.