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:我们的核心想法是利用LIDAR点云的强大空间信号更好地利用未贴标签的数据。 我们建议激光Mix将不同LIDAR扫描的激光束混在一起,然后鼓励模型在混合之前和之后作出一致和自信的预测。 我们的框架有三个吸引人的特性:(1) 通用的:激光Mix是激光Mix对LIDAR的表示方式(例如,测距视图和 voxel)的敏感度,因此我们的 SSLL框架可以普遍适用。 (2) 统计依据:我们提供详细分析,从理论上解释拟议框架的适用性。(3) 有效:对广受欢迎的LIDAR分解数据集(Nuscenes、SemanticKITTI和ScribikKITTI)的全面实验分析可以显示我们的效力和优越性。 显而易见,我们通过完全超前的SLISLAAR 表示(例如,测距视图和 voxel) 的表示方式,因此我们的SLSLSL框架可以普遍适用。 (2) 我们提供详细分析,从理论上解释拟议框架的适用性分析。 3:对流行的LIDAR分解数据集(nScoveralalal-eferviewalationalalationalational) 10)可以大大改善10的标值, 10x 的标值比比比值比值为2到5。