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.

翻译：密集标注激光雷达点云成本高昂，这限制了全监督学习方法的可扩展性。本文研究了激光雷达分割中尚未充分探索的半监督学习（SSL）。我们的核心思想是利用激光雷达点云中丰富的空间线索以更好地利用未标注数据。我们提出LaserMix方法，通过混合来自不同激光雷达扫描的激光束，并促使模型在混合前后保持一致且置信的预测。该框架具有三大优势：1）通用性：LaserMix不依赖于特定激光雷达表示（如距离视图与体素），因此可普遍应用于不同SSL框架。2）理论严谨性：我们提供详细分析，从理论上解释所提框架的适用性。3）高效性：在主流激光雷达分割数据集（nuScenes、SemanticKITTI和ScribbleKITTI）上的全面实验分析证明了我们的有效性与优越性。值得注意的是，我们仅使用全监督方法2倍至5倍更少的标注数据即可达到竞争性结果，并平均提升纯监督基线10.8%的性能。期望这一简洁而高效的框架能推动半监督激光雷达分割领域的未来研究。代码已公开。