论文标题
Lasermix半监督激光雷达语义分段
LaserMix for Semi-Supervised LiDAR Semantic Segmentation
论文作者
论文摘要
密集注释的LiDAR点云是昂贵的，这限制了完全监督的学习方法的可扩展性。在这项工作中，我们研究了激光雷达分割中未充满偏移的半监督学习（SSL）。我们的核心思想是利用LiDar Point Clouds的强大空间提示来更好地利用未标记的数据。我们建议Lasermix从不同的激光扫描中混合激光束，然后鼓励模型在混合前后做出一致和自信的预测。我们的框架具有三个吸引人的属性：1）通用：Lasermix对LIDAR表示不可知（例如，范围视图和体素），因此可以普遍应用我们的SSL框架。 2）从统计上讲：我们提供了详细的分析，以理论上解释所提出的框架的适用性。 3）有效：对流行的LiDAR分割数据集（Nuscenes，Semantickitti和Scribblekitti）的全面实验分析证明了我们的有效性和优势。值得注意的是，我们在标签少2倍至5倍的同行中获得了竞争成果，并平均将仅监督的基线提高了10.8％。我们希望这个简洁而高性能的框架可以促进半监督的激光雷达细分的未来研究。代码公开可用。
Densely annotating LiDAR point clouds is costly, which restrains the scalability of fully-supervised learning methods. In this work, we study the underexplored semi-supervised learning (SSL) in LiDAR segmentation. Our core idea is to leverage the strong spatial cues of LiDAR point clouds to better exploit unlabeled data. We propose LaserMix to mix laser beams from different LiDAR scans, and then encourage the model to make consistent and confident predictions before and after mixing. Our framework has three appealing properties: 1) Generic: LaserMix is agnostic to LiDAR representations (e.g., range view and voxel), and hence our SSL framework can be universally applied. 2) Statistically grounded: We provide a detailed analysis to theoretically explain the applicability of the proposed framework. 3) Effective: Comprehensive experimental analysis on popular LiDAR segmentation datasets (nuScenes, SemanticKITTI, and ScribbleKITTI) demonstrates our effectiveness and superiority. Notably, we achieve competitive results over fully-supervised counterparts with 2x to 5x fewer labels and improve the supervised-only baseline significantly by 10.8% on average. We hope this concise yet high-performing framework could facilitate future research in semi-supervised LiDAR segmentation. Code is publicly available.
