Reconstruction of Triangular Meshes from Spatial Point Clouds

Three-dimensional reconstruction from spatial point cloud data is a fundamental task in computer graphics and 3D vision, where triangular meshes serve as the most common representation form. The reconstruction quality directly impacts the accuracy and efficiency of subsequent applications. The reconstruction method based on multi-seed points and prioritized growth in flat regions provides a novel solution for efficient point-cloud-to-mesh conversion.

Traditional point cloud reconstruction methods typically expand from a single initial point, often resulting in holes or distortions due to complex local geometric features. In contrast, our method better adapts to geometric characteristics across different regions of the point cloud by introducing a multi-seed strategy. During implementation, the algorithm prioritizes selecting seed points from low-curvature (flatter) regions based on point cloud curvature distribution, as these areas generally exhibit more stable geometric features and yield more reliable reconstruction results.

The flat-region priority growth strategy is grounded in two key observations: first, flat regions usually have higher point cloud density and relatively lower data noise; second, expansion from flat regions more easily maintains the manifold properties of the mesh. During the growth process, the algorithm dynamically evaluates the geometric complexity of adjacent areas, prioritizing the connection of triangular patches in flat regions before progressively advancing to complex areas. This adaptive growth sequence significantly reduces ambiguity during reconstruction.

For high-curvature regions (such as edges or corners), the algorithm delays processing until the mesh structure of surrounding flat areas stabilizes, then connects these special regions to the reconstructed parts through constrained optimization. This hierarchical approach ensures reconstruction efficiency while avoiding negative impacts on overall mesh quality from complex regions.

The advantages of this method include: significantly improved topological correctness of reconstructed meshes through intelligent seed selection and optimized growth sequence; particular suitability for point cloud data containing large flat areas (e.g., building facades, industrial components); and enhanced robustness against noise and uneven point distribution. Future improvements could incorporate deep learning techniques to automate the learning of seed point selection and growth strategies.