Automated semantic understanding of dense point clouds is a prerequisite for Scan-to-BIM pipelines, digital twin construction, and as-built verification--core tasks in the digital transformation of the construction industry. Yet for industrial mechanical, electrical, and plumbing (MEP) facilities, this challenge remains largely unsolved: TLS acquisitions of water treatment plants, chiller halls, and pumping stations exhibit extreme geometric ambiguity, severe occlusion, and extreme class imbalance that architectural benchmarks (e.g., S3DIS or ScanNet) cannot adequately represent. We present Industrial3D, a terrestrial LiDAR dataset comprising 612 million expertly labelled points at 6 mm resolution from 13 water treatment facilities. At 6.6x the scale of the closest comparable MEP dataset, Industrial3D provides the largest and most demanding testbed for industrial 3D scene understanding to date. We further establish the first industrial cross-paradigm benchmark, evaluating nine representative methods across fully supervised, weakly supervised, unsupervised, and foundation model settings under a unified benchmark protocol. The best supervised method achieves 55.74% mIoU, whereas zero-shot Point-SAM reaches only 15.79%--a 39.95 percentage-point gap that quantifies the unresolved domain-transfer challenge for industrial TLS data. Systematic analysis reveals that this gap originates from a dual crisis: statistical rarity (215:1 imbalance, 3.5x more severe than S3DIS) and geometric ambiguity (tail-class points share cylindrical primitives with head-class pipes) that frequency-based re-weighting alone cannot resolve. Industrial3D, along with benchmark code and pre-trained models, will be publicly available at https://github.com/pointcloudyc/Industrial3D.

翻译：稠密点云的自动化语义理解是扫描到建筑信息模型（Scan-to-BIM）流程、数字孪生构建及竣工验证——这些建筑业数字化转型核心任务——的先决条件。然而，对于工业机械、电气和管道（MEP）设施，这一挑战仍远未解决：水处理厂、冷水机厅和泵站的地面激光扫描（TLS）数据呈现出极端的几何模糊性、严重遮挡及类别极端不平衡，这是现有建筑领域基准（如S3DIS或ScanNet）无法充分表征的。本文提出Industrial3D，一个包含来自13个水处理设施、以6毫米分辨率采集的6.12亿个经过专家标注点的地面LiDAR数据集。其规模是最近似MEP数据集的6.6倍，为目前工业三维场景理解提供了最大且最具挑战性的测试平台。我们进一步建立了首个工业跨范式基准，在统一基准协议下，于全监督、弱监督、无监督及基础模型设定中评估了九种代表性方法。最优监督方法达到了55.74%的mIoU，而零样本Point-SAM仅实现15.79%——这39.95个百分点的差距量化了工业TLS数据中尚未解决的域迁移挑战。系统性分析表明，这一差距源于双重危机：统计稀有性（215:1的不平衡度，比S3DIS严重3.5倍）与几何模糊性（尾类点与头类管道共享圆柱形基元），仅凭基于频率的重加权无法解决。Industrial3D及其基准代码、预训练模型将在https://github.com/pointcloudyc/Industrial3D公开提供。