Multi-Space Alignments Towards Universal LiDAR Segmentation
<|reference_start|>Multi-Space Alignments Towards Universal LiDAR Segmentation: A unified and versatile LiDAR segmentation model with strong robustness and generalizability is desirable for safe autonomous driving perception. This work presents M3Net, a one-of-a-kind framework for fulfilling multi-task, multi-dataset, multi-modality LiDAR segmentation in a universal manner using just a single set of parameters. To better exploit data volume and diversity, we first combine large-scale driving datasets acquired by different types of sensors from diverse scenes and then conduct alignments in three spaces, namely data, feature, and label spaces, during the training. As a result, M3Net is capable of taming heterogeneous data for training state-of-the-art LiDAR segmentation models. Extensive experiments on twelve LiDAR segmentation datasets verify our effectiveness. Notably, using a shared set of parameters, M3Net achieves 75.1%, 83.1%, and 72.4% mIoU scores, respectively, on the official benchmarks of SemanticKITTI, nuScenes, and Waymo Open.<|reference_end|>
@article{liu2024multi-space,
    title={Multi-Space Alignments Towards Universal LiDAR Segmentation},
    author={Youquan Liu and Lingdong Kong and Xiaoyang Wu and Runnan Chen and Xin
  Li and Liang Pan and Ziwei Liu and Yuexin Ma},
    journal={arXiv preprint arXiv:2405.01538},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.01538},
    primaryClass={cs.CV cs.LG cs.RO}
}