无需人工标注，用自监督方法实现跨传感器的地面分割。
TerraSeg: Self-Supervised Ground Segmentation for Any LiDAR
- 通过自优化生成伪标签，实现无监督训练。
- 在15种传感器数据上训练，跨域性能领先。
- 适合需要低成本部署的自动驾驶与机器人场景。
LiDAR感知是机器人理解三维环境的基础任务之一，地面分割对场景理解与导航至关重要。然而，现有方法要么针对特定传感器设计，要么依赖昂贵的逐点人工标注，严重限制了泛化性与可扩展性。为此，我们提出TerraSeg，首个无监督、领域无关的LiDAR地面分割模型。我们在OmniLiDAR——一个整合12个主流公开基准的数据集上训练，该数据集包含近2200万原始扫描，覆盖15种不同传感器型号，提供了前所未有的多样性。为避免人工标注，我们提出PseudoLabeler模块，通过每帧运行时自监督优化生成高质量地面与非地面标签。大量实验表明，TerraSeg在nuScenes、SemanticKITTI和Waymo Perception上均达到当前最佳性能，且具备实时推理能力。代码与模型权重已开源。
原文摘要 · Abstract (English)
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. However, existing methods are either handcrafted for specific sensor configurations or rely on costly per-point manual labels, severely limiting their generalization and scalability. To overcome this, we introduce TerraSeg, the first self-supervised, domain-agnostic model for LiDAR ground segmentation. We train TerraSeg on OmniLiDAR, a unified large-scale dataset that aggregates and standardizes data from 12 major public benchmarks. Spanning almost 22 million raw scans across 15 distinct sensor models, OmniLiDAR provides unprecedented diversity for learning a highly generalizable ground model. To supervise training without human annotations, we propose PseudoLabeler, a novel module that generates high-quality ground and non-ground labels through self-supervised per-scan runtime optimization. Extensive evaluations demonstrate that, despite using no manual labels, TerraSeg achieves state-of-the-art results on nuScenes, SemanticKITTI, and Waymo Perception while delivering real-time performance. Our code and model weights are publicly available.
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