  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.
