Source-Free Domain Adaptation for Geospatial Point Cloud Semantic Segmentation

TL;DR

This paper introduces LoGo, a source-free domain adaptation framework for geospatial point cloud segmentation, combining local class-balanced prototypes and global distribution alignment to improve performance on target data without source data access.

Contribution

The paper presents a novel SFUDA method with class-balanced prototype estimation and optimal transport-based global alignment tailored for geospatial point clouds, addressing long-tail class issues.

Findings

Effective mitigation of long-tail class bias

Improved segmentation accuracy on target domain

Robust pseudo-label filtering mechanism

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.