Industrial point cloud segmentation for Digital Twin construction faces a persistent challenge: safety-critical components such as reducers and valves are systematically misclassified. These failures stem from two compounding factors: such components are rare in training data, yet they share identical local geometry with dominant structures like pipes. This work identifies a dual crisis unique to industrial 3D data extreme class imbalance 215:1 ratio compounded by geometric ambiguity where most tail classes share cylindrical primitives with head classes. Existing frequency-based re-weighting methods address statistical imbalance but cannot resolve geometric ambiguity. We propose spatial context constraints that leverage neighborhood prediction consistency to disambiguate locally similar structures. Our approach extends the Class-Balanced (CB) Loss framework with two architecture-agnostic mechanisms: (1) Boundary-CB, an entropy-based constraint that emphasizes ambiguous boundaries, and (2) Density-CB, a density-based constraint that compensates for scan-dependent variations. Both integrate as plug-and-play modules without network modifications, requiring only loss function replacement. On the Industrial3D dataset (610M points from water treatment facilities), our method achieves 55.74% mIoU with 21.7% relative improvement on tail-class performance (29.59% vs. 24.32% baseline) while preserving head-class accuracy (88.14%). Components with primitive-sharing ambiguity show dramatic gains: reducer improves from 0% to 21.12% IoU; valve improves by 24.3% relative. This resolves geometric ambiguity without the typical head-tail trade-off, enabling reliable identification of safety-critical components for automated knowledge extraction in Digital Twin applications.

翻译：面向数字孪生构建的工业点云分割面临一个持续存在的挑战：减速器、阀门等安全关键部件存在系统性误分类。这些失效源于两个叠加因素：此类部件在训练数据中极为稀少，且与管道等主导结构具有完全相同的局部几何特征。本研究揭示了工业三维数据特有的双重困境——215:1的极端类别不平衡与几何歧义性（多数尾部类别与头部类别共享圆柱形基元）的叠加。现有基于频率的重加权方法虽能处理统计不平衡，却无法解决几何歧义问题。我们提出空间上下文约束机制，利用邻域预测一致性来区分局部相似结构。该方法在类别平衡（CB）损失框架基础上扩展出两种架构无关的机制：（1）边界-CB：基于信息熵的约束，聚焦歧义边界区域；（2）密度-CB：基于点云密度的约束，补偿扫描相关的数据变异。两者均以即插即用模块形式集成，无需修改网络架构，仅需替换损失函数。在Industrial3D数据集（来自水处理设施的6.1亿个点）上，本方法实现了55.74%的平均交并比，尾部类别性能相对提升21.7%（29.59% vs. 基线24.32%），同时保持头部类别精度（88.14%）。存在基元共享歧义的部件获得显著提升：减速器交并比从0%提升至21.12%；阀门相对提升24.3%。该方法在避免典型头尾类别权衡的前提下解决了几何歧义问题，为数字孪生应用中的自动化知识提取实现了安全关键部件的可靠识别。