GSANet: Geometric Structure-Aware Siamese Network for 3D Change Detection

Three-dimensional (3D) point cloud change detection is essential for urban monitoring and environmental analysis, yet existing methods mainly rely on point-wise semantic differences and overlook change–unchange boundaries and object edges—key geometric cues for precise localization, especially for subtle or gradual changes. To address this, we propose GSANet, a Geometric Structure-Aware Siamese Network that explicitly integrates boundary and edge priors into sampling and feature learning. First, a Boundary-Aware Subsampling (BAS) strategy preserves key points near change boundaries while reducing redundancy, and a Boundary-Aware Binary Cross-Entropy (BA-BCE) loss assigns higher supervision weights to boundary points, enhancing learning in ambiguous regions. Second, an Edge-Aware Siamese Network captures robust local shapes by embedding edge priors into feature extraction, incorporating Edge-Aware Adaptive Graph Convolution, Edge-Aware Downsampling, and Cross-Attention Upsampling to maintain structural consistency across temporal branches. Additionally, a Difference Enhancement Module (DEM) amplifies feature discrepancies between bitemporal point clouds, improving sensitivity to subtle changes. Extensive experiments on a street-level dataset and urban dataset show our method outperforms state-of-the-art approaches.