CVPR 2023, highlight paper - LaserMix for Semi-Supervised LiDAR Semantic Segmentation

In this episode we discuss LaserMix for Semi-Supervised LiDAR Semantic Segmentation by Lingdong Kong, Jiawei Ren, Liang Pan, Ziwei Liu. The paper proposes a semi-supervised learning framework, called LaserMix, for LiDAR semantic segmentation, leveraging the strong spatial cues of LiDAR point clouds to better exploit unlabeled data. The framework mixes laser beams from different LiDAR scans and then encourages the model to make consistent and confident predictions before and after mixing. The framework is demonstrated to be effective with comprehensive experimental analysis on popular LiDAR segmentation datasets, achieving competitive results over fully-supervised counterparts with fewer labels and improving the supervised-only baseline significantly by 10.8%.