UniLiDAR: Bridge the domain gap among different LiDARs for continual
  learning
Zikun Xu Jianqiang Wang Shaobing Xu
LiDAR-based 3D perception algorithms have evolved rapidly alongside the
emergence of large datasets. Nonetheless, considerable performance degradation
often ensues when models trained on a specific dataset are applied to other
datasets or real-world scenarios with different LiDAR. This paper aims to
develop a unified model capable of handling different LiDARs, enabling
continual learning across diverse LiDAR datasets and seamless deployment across
heterogeneous platforms. We observe that the gaps among datasets primarily
manifest in geometric disparities (such as variations in beams and point
counts) and semantic inconsistencies (taxonomy conflicts). To this end, this
paper proposes UniLiDAR, an occupancy prediction pipeline that leverages
geometric realignment and semantic label mapping to facilitate multiple
datasets training and mitigate performance degradation during deployment on
heterogeneous platforms. Moreover, our method can be easily combined with
existing 3D perception models. The efficacy of the proposed approach in
bridging LiDAR domain gaps is verified by comprehensive experiments on two
prominent datasets: OpenOccupancy-nuScenes and SemanticKITTI. UniLiDAR elevates
the mIoU of occupancy prediction by 15.7% and 12.5%, respectively, compared to
the model trained on the directly merged dataset. Moreover, it outperforms
several SOTA methods trained on individual datasets. We expect our research to
facilitate further study of 3D generalization, the code will be available soon.