Semantic segmentation of 3D geospatial point clouds is pivotal for remote sensing applications. However, variations in geographic patterns across regions and data acquisition strategies induce significant domain shifts, severely degrading the performance of deployed models. Existing domain adaptation methods typically rely on access to source-domain data. However, this requirement is rarely met due to data privacy concerns, regulatory policies, and data transmission limitations. This motivates the largely underexplored setting of source-free unsupervised domain adaptation (SFUDA), where only a pretrained model and unlabeled target-domain data are available. In this paper, we propose LoGo (Local-Global Dual-Consensus), a novel SFUDA framework specifically designed for geospatial point clouds. At the local level, we introduce a class-balanced prototype estimation module that abandons conventional global threshold filtering in favor of an intra-class independent anchor mining strategy. This ensures that robust feature prototypes can be generated even for sample-scarce tail classes, effectively mitigating the feature collapse caused by long-tailed distributions. At the global level, we introduce an optimal transport-based global distribution alignment module that formulates pseudo-label assignment as a global optimization problem. By enforcing global distribution constraints, this module effectively corrects the over-dominance of head classes inherent in local greedy assignments, preventing model predictions from being severely biased towards majority classes. Finally, we propose a dual-consistency pseudo-label filtering mechanism. This strategy retains only high-confidence pseudo-labels where local multi-augmented ensemble predictions align with global optimal transport assignments for self-training.

翻译：三维地理空间点云的语义分割对于遥感应用至关重要。然而，不同区域的地理模式差异以及数据采集策略的多样性会导致显著的域偏移，严重降低已部署模型的性能。现有的域自适应方法通常依赖于对源域数据的访问。然而，由于数据隐私问题、监管政策以及数据传输限制，这一要求往往难以满足。这促使我们探索一个尚未被充分研究的设定：源数据缺失的无监督域自适应，其中仅有一个预训练模型和未标记的目标域数据可用。本文提出LoGo（局部-全局双重共识），一个专为地理空间点云设计的新型SFUDA框架。在局部层面，我们引入了一个类别平衡的原型估计模块，该模块摒弃了传统的全局阈值过滤方法，转而采用类内独立锚点挖掘策略。这确保了即使对于样本稀少的尾部类别，也能生成鲁棒的特征原型，有效缓解由长尾分布引起的特征崩溃问题。在全局层面，我们引入了一个基于最优传输的全局分布对齐模块，该模块将伪标签分配构建为一个全局优化问题。通过施加全局分布约束，该模块有效纠正了局部贪婪分配中固有的头部类别过度主导问题，防止模型预测严重偏向多数类别。最后，我们提出了一种双重一致性的伪标签过滤机制。该策略仅保留那些局部多重增强集成预测与全局最优传输分配结果一致的高置信度伪标签，用于自训练。