Towards Efficient Semi-Supervised Semantic Segmentation for Solid-State LiDAR Point Clouds

Abstract

LiDAR-based 3D semantic segmentation is a critical task in autonomous driving, but its scalability is limited by the reliance on large-scale labeled datasets. Semi-supervised learning (SSL) offers a potential solution by leveraging unlabeled data. However, most existing SSL segmentation methods are designed for mechanical spinning LiDAR (MSLR) and fail to generalize well to solid-state LiDAR (SSLR) due to different scanning patterns and point cloud distributions. To address this challenge, we propose SSLiMix, a novel semi-supervised segmentation method with checkerboard mixing for solid-state LiDAR. Unlike prior MSLR-oriented methods, SSLiMix employs 2D grid partitioning with checkerboard mixing to adapt to SSLR’s dense and uniform point clouds, thereby preserving spatial consistency even when beam-based augmentations fail. Additionally, we introduce a hierarchical confidence-aware pseudo-labeling mechanism (HCAP), which classifies pseudolabels by confidence and applies targeted processing to enhance pseudo-label reliability. Experiments on the PandaSet dataset show that SSLiMix improves mIoU by 11.3% over the fullysupervised baseline using only 1% labeled data, demonstrating its effectiveness in low-label regimes and providing a strong benchmark for semi-supervised SSLR segmentation.