The aim of three-dimensional change detection technology is to identify temporal feature alterations from multi-temporal point clouds within identical geographical scenes, which plays a crucial role in understanding surface dynamics. Conversely, contemporary deep-learning-based approaches always underperform in complex urban settings characterized by non-uniform point density, occlusions, and multi-viewpoint overlaps, which arises from insufficient incorporation of local geometric intricacies and global contextual dependencies. Therefore, we propose GS-EANet (geometric structure refinement and entropy-attention network) which effectively models scene-aware geometric complexity for robust 3D change detection. During the encoding stage, GS-EANet combines local complexity analysis module and stratified sampling module, which uses three key features including point curvature, density, and information entropy, to describe the spatial arrangement of points in a neighborhood. This multi-feature fusion serves as prior knowledge for calculating sampling probabilities. The stratified sampling module based on sampling probability notably optimizes geometric structures while down-sampling, thereby achieving an optimal balance between global scene coverage and emphasis on critical regions. Furthermore, we advance conventional dual-path attention mechanisms by incorporating information-entropy, which dynamically steers spatial and channel attention toward structurally complex regions, substantially enhancing global contextual representation. Comprehensive evaluations on the SLPCCD and Urb3DCD datasets demonstrate that GS-EANet surpasses seven state-of-the-art baseline methods in both quantitative and qualitative assessments. Notably, on the SLPCCD dataset, GS-EANet achieves a mean intersection over union (mIoU) of 17.01% higher than 3DCDNet. Moreover, GS-EANet outperforms the previous best-performing model, Ms-DANet, by 3.35%. The proposed approach effectively mitigates false or missed detections caused by density variations, occlusions, and viewpoint overlaps, establishing a new state-of-the-art in 3D change detection for complex urban environment.
