An edge-sensitive simplification method for scanned point clouds
Shifan Liu, Jin Tao Liang, Maodong Ren, JingBin He, Chunyuan Gong, Lu Wang, Zehua Miao · Measurement Science and Technology · 2019
Abstract Due to the huge number of points on three-dimensional point clouds captured by optical scanning devices, point-based simplification is a crucial step in model reconstruction. However, the loss of edge features of industrial parts after such simplification reduces reconstruction accuracy. This paper presents an edge-sensitive, point-based simplification method to eliminate redundant points and preserve more edge feature details. Firstly, a new geometrical descriptor is created for each point to generate a geometrical domain. A clustering scheme is then designed by applying two different clustering algorithms, to split the point cloud in the geometrical domain and the spatial domain respectively. The proposed method is capable of preserving edge features well, while reducing the original number of points to 10% or even 5%. The proposed method is compared with other simplification methods and the experimental results indicate that it performs better in simplifying industrial parts.