An Empirical Study of Training State-of-the-Art LiDAR Segmentation Models
<|reference_start|>An Empirical Study of Training State-of-the-Art LiDAR Segmentation Models: In the rapidly evolving field of autonomous driving, precise segmentation of LiDAR data is crucial for understanding complex 3D environments. Traditional approaches often rely on disparate, standalone codebases, hindering unified advancements and fair benchmarking across models. To address these challenges, we introduce MMDetection3D-lidarseg, a comprehensive toolbox designed for the efficient training and evaluation of state-of-the-art LiDAR segmentation models. We support a wide range of segmentation models and integrate advanced data augmentation techniques to enhance robustness and generalization. Additionally, the toolbox provides support for multiple leading sparse convolution backends, optimizing computational efficiency and performance. By fostering a unified framework, MMDetection3D-lidarseg streamlines development and benchmarking, setting new standards for research and application. Our extensive benchmark experiments on widely-used datasets demonstrate the effectiveness of the toolbox. The codebase and trained models have been publicly available, promoting further research and innovation in the field of LiDAR segmentation for autonomous driving.<|reference_end|>
@article{sun2024an,
    title={An Empirical Study of Training State-of-the-Art LiDAR Segmentation
  Models},
    author={Jiahao Sun and Chunmei Qing and Xiang Xu and Lingdong Kong and Youquan
  Liu and Li Li and Chenming Zhu and Jingwei Zhang and Zeqi Xiao and Runnan
  Chen and Tai Wang and Wenwei Zhang and Kai Chen},
    journal={arXiv preprint arXiv:2405.14870},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14870},
    primaryClass={cs.CV cs.RO}
}