A novel point cloud simplification method with integration of multiple-feature fusion and density uniformity

In recent years, 3D point cloud technology has played an increasingly important role in the field of casting parts detection. Due to the huge amount of high-precision point cloud data, simplification is often necessary before 3D reconstruction to improve computational efficiency. However, most of the existing point cloud simplification algorithms often blur edge details and produce holes in the final 3D reconstruction. To avoid these problems, a novel point cloud simplification method is proposed here. To accurately describe the geometric and texture features, a multi-featured fusion method was first developed to integrate the curvature, local projection distance, normal vector and local color differences. As this approach uses multiple types of geometric features, it accurately distinguishes sharp and smooth edges. Moreover, due to the use of local color difference, the texture features of the point cloud are recognizable. Then, to evaluate the density of the non-uniform point cloud, a globular neighborhood search strategy was used to estimate the density of the point cloud itself. Since the number of points in the spherical neighborhood reflected the sparsity of the point cloud, this method accurately represented the density of a point cloud with an uneven distribution. Finally, the feature fusion and density uniformity were integrated, allowing the use of these preserved points to retain more edge details of the point cloud in the final 3D reconstruction. This approach also avoided holes in the final 3D reconstruction. On the point cloud data of Anchor, Fantisk and Humana, the root mean square error of the proposed method reduces 40%, 32% and 19% than the best algorithm among the K-means clustering, Grid average, and Graph based method, respectively.