Geometric feature-enhanced reconstruction and refinement of photogrammetric mesh models in man-made environments

The automatic reconstruction of photogrammetric mesh models often suffers from issues such as erroneous adhesion of different planes, local distortions, and the loss of edge details. This study aims to improve mesh quality by incorporating three-dimensional (3D) line segments and plane regions as geometric constraints during mesh refinement. Using previously obtained 3D line segments, depth, and normal information, accurate 3D plane regions are extracted to complete missing sections of the dense point cloud, with depth-based outlier removal applied. Correspondences between mesh model vertices and the 3D line segments and plane regions are established, followed by mesh refinement through an energy function that integrates photo-consistency, line and plane constraints, and regularization. Experiments conducted on four open-source datasets demonstrate that the proposed method substantially improves 3D plane region extraction and point cloud completion. The refined mesh model achieves a 24.30% reduction in the average distance related to 3D lines and a 15.75% reduction for 3D planes compared with the original model, confirming the effectiveness of incorporating 3D line and plane constraints in enhancing reconstruction accuracy.