Extraction of Line Segments from Indoor Point Clouds under Building Geometric Regularization Constraints

To address the issues of redundancy, low integrity, and insufficient precision in 3D line segment extraction from building indoor point clouds, a 3D line segment extraction and optimization algorithm incorporating building geometric regularization constraints is proposed. Initially, a region growing and merging algorithm is applied to segment point clouds into surface patches upon which 2D profile lines are extracted through plane projection, followed by the 3D-2D back-projection to obtain the initial set of building profile line segments. Next, leveraging architectural geometric regularization features, a multi-constraint model was designed that integrates the building’s principal direction, intersection lines of adjacent facets, collinearity of building contours, and orthogonality of architectural structures. Through this segment optimization model, high-precision extraction of building structure contour lines was achieved, significantly enhancing their overall accuracy and completeness. The optimization process first analyzes the spatial adjacency relationships between patches and corrects the orientation of line segments that are collinear with intersecting lines of patches. It then analyzes the alignment between the building's principal directions and the extracted contour line segments to effectively filter out redundant non-structural segments, while subsequently evaluating the spatial topological relationships between line segments to mitigate the discontinuity and angular deviation, thereby providing detailed geometric features for the indoor 3D reconstruction of existing buildings. Quantitative analysis conducted on two indoor scenes demonstrates a 6.61% improvement in average completeness and an 11.72% reduction in average redundancy compared to traditional methods. Complementary qualitative analysis of two additional indoor scenes further validates its effectiveness in addressing complex building structures, highlighting its robust adaptability and reliability across diverse interior architectural contexts.