A Novel Triangulation-Based Method for Hole Filling in 3D Point Clouds Using Approximation Curve Fitting

In point cloud data processing, preprocessing is a critical step, particularly for addressing incomplete data caused by occlusions or limited visibility during acquisition. To enhance the completeness of point cloud models, this paper proposes a method for reconstructing missing regions using approximation curves. The approach begins by deriving control points from the existing point cloud, which are used to construct curves that closely conform to the surface geometry. Control points are computed through triangulation and spatial analysis, ensuring that the generated curves align with the local structure of the data. Uniform sampling is then applied along each curve to produce a set of points that effectively fill the missing regions. Experimental results demonstrate that the proposed method can successfully reconstruct surface segments with consistent spacing and high accuracy, making the data suitable for downstream applications such as 3D modeling, object classification, and structural analysis.