RePL: Pseudo-label Refinement for Semi-supervised LiDAR Semantic Segmentation

Semi-supervised learning for LiDAR semantic segmentationoften suffers from error propagation and confirmation bias caused bynoisy pseudo-labels. To tackle this chronic issue, we introduce RePL, anovel framework that enhances pseudo-label quality by identifying andcorrecting potential errors in pseudo-labels through masked reconstruc-tion, along with a dedicated training strategy. We also provide a theoret-ical analysis demonstrating the condition under which the pseudo-labelrefinement is beneficial, and empirically confirm that the condition ismild and clearly met by RePL. Extensive evaluations on the nuScenes-lidarseg and SemanticKITTI datasets show that RePL improves pseudo-label quality substantially, and in consequence, achieves the state of theart in semi-supervised LiDAR semantic segmentation.