Optimal Feature-Guided Position-Shape Dual Optimization for Building Point Cloud Facade Detail Enhancement

Dense three-dimensional point clouds are the cornerstone of modern architectural 3D reconstruction, containing a wealth of semantic structural information about building facades. However, current methods struggle to automatically and accurately extract the complex detailed structures of building facades from unstructured point clouds, with detailed facade modeling often relying heavily on manual interaction. This study introduces an efficient method for semantic structural detail enhancement of building facade point clouds, achieved through feature-guided dual-layer optimization of position and shape. The proposed framework addresses three key challenges: (1) robust extraction of facade semantic feature point clouds to effectively perceive the underlying geometric features of facade structures; (2) improved grouping of similarly structured objects using Hausdorff distance discrimination, overcoming the impact of point cloud omissions and granularity differences; (3) position-shape double optimization for facade enhancement, achieving detailed structural optimization. Validated on three typical datasets, the proposed method not only achieved 98.5% accuracy but also effectively supplemented incomplete scan results. It effectively optimizes semantic structures that widely exist and have the characteristic of repeated appearance on building facades, providing robust support for smart city construction and analytical applications.