MVSE-3D: multi-view semantic edge optimization for precision recovery of 3D building geometry

Dense matching algorithms face challenges in disparity discontinuity regions and weak/repetitive texture areas, leading to surface irregularities, geometric detail loss, and void artifacts in oblique photogrammetric 3D models. To address these limitations, this study proposes a novel geometry optimization framework guided by image-space semantic edge features through geometric mapping between 2D imagery and 3D models. Our methodology initiates with extracting structural point clouds from building models through geometric-topological analysis and multi-view texture coherence assessment. A hybrid feature extraction strategy combines Line Segment Detector (LSD)-based structural line detection with deep learning-enhanced edge characterization across multi-perspective imagery. The framework implements a multi-stage refinement process: 1) Depth-First Search (DFS)-based edge component labeling with multi-view geometric constraints, 2) Non-structural component rejection through attribute-driven filtering and projective geometry analysis, 3) Adaptive dynamic buffer zones with Markov Random Field (MRF) optimization for optimal structural line association, and 4) Epipolar-constrained position refinement using multi-homography geometric verification. Experimental validation demonstrates our method's superiority in architectural point cloud localization accuracy, achieving 20.43% average reduction in point-to-mesh distance compared to conventional approaches, and showing significant improvements in surface continuity and void reduction. The framework mitigates local deformations while preserving architectural details, establishing a robust solution for photogrammetric 3D model optimization.