探讨一种基于骨架匹配的建筑点云融合方法，旨在解决建筑实景三维模型中模型缺失的挑战。该方法首先通过建筑实景三维逆向建模，利用所得的逆向点云数据结合原有建筑设计数据生成正向点云模型。随后，运用基于旋转对称轴（rotational symmetry axis, ROSA）的方法分别从建筑逆向点云模型和正向点云模型中提取模型骨架线。最终，通过正逆向骨架线匹配，实现建筑正向和逆向点云模型的融合，进而重构出完整性较高的三维模型。实验验证显示，该方法显著减少了模型缺失区域，为建筑实景三维建模和逆向工程领域提供了新的思路和方法。

A skeleton-based architectural point cloud fusion method was explored to address the challenges of model incompleteness in real-world 3D architectural models. The process begins with reverse modeling of the architectural scene using 3D point clouds. The acquired reverse point cloud data was combined with the original architectural design data to generate a forward point cloud model. A method based on the rotational symmetry axis (ROSA) was then applied to extract skeleton lines from both the reverse and forward point cloud models. The fusion of the forward and reverse point cloud models was achieved by skeleton matching, resulting in a reconstructed 3D model with improved completeness. Experimental validation shows that this method significantly reduces areas of model incompleteness, providing new insights and methods for 3D modeling and reverse engineering in architectural reality capture.