TerraSeg: Self-Supervised Ground Segmentation for Any LiDAR

LiDAR perception is fundamental to robotics, enabling machines to understand their environment in 3D. A crucial task for LiDAR-based scene understanding and navigation is ground segmentation. Existing methods are either handcrafted for specific LiDAR configurations or require costly per-point manual labels, limiting generalization and scalability. We introduce TerraSeg, establishing the first self-supervised LiDAR foundation model for ground segmentation. We train TerraSeg on OmniLiDAR, a unified large-scale dataset that aggregates and standardizes LiDAR data from nine major public benchmarks, spanning over 20 million raw scans and 11 distinct sensor models, providing unprecedented diversity for learning a generalizable ground model. OmniLiDAR is pseudo-labeled by our PseudoLabeler, a novel self-supervised module that generates high-quality ground/non-ground labels through per-scan runtime optimization. Without any manual labels, TerraSeg achieves state-of-the-art results on nuScenes, SemanticKITTI, and Waymo Perception, and delivers close-to-real-time performance. Our code and models will be publicly released upon paper acceptance.