Abstract
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.
Abstract (translated)
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，不同地区的地理模式变化和数据采集策略导致了显著的领域偏移（domain shift），这严重降低了已部署模型的表现能力。现有的领域适应方法通常依赖于源域数据的访问权限。但由于数据隐私问题、监管政策以及数据传输限制，这一要求往往难以满足。因此，研究者们开始探索无需源域监督信息的无监督领域自适应（Source-Free Unsupervised Domain Adaptation, SFUDA）场景，在这种场景下，仅有预训练模型和未标注的目标区域数据可用。 本文提出了一种新的SFUDA框架——LoGo（Local-Global Dual-Consensus），专门针对地理空间点云。在局部层面，我们引入了一个平衡的原型估计模块，放弃了传统的全局阈值过滤方法，转而采用一种基于类内独立锚点挖掘策略的方法。这确保了即使对于样本稀缺的小类别，也能生成稳健的特征原型，从而有效地缓解由长尾分布带来的特征坍缩问题。 在全局层面，我们引入了一个基于最优传输（optimal transport）的全局分布对齐模块，该模块将伪标签分配形式化为一个全局优化问题。通过施加全局分布约束，这一模块能够有效纠正局部贪婪分配中头部类别占主导的问题，防止模型预测严重偏向多数类。 最后，我们提出了一种双一致性伪标签过滤机制。这种方法保留了那些在多增广集成预测与全球最优传输分配一致的高置信度伪标签用于自我训练。
URL
https://arxiv.org/abs/2601.08375
