Points using Localized Distance for Contour Generation from Point Cloud for 3D Printing

Abstract

We present a robust and simple method to select the most correlated points (in each layer) for layered contour generation from a 3D point cloud model in additive manufacturing. The contour projection of each layer point uses a planar least square projection technique. One critical step in contour projection is that the correlation between each projection point and other points (weights) on a slicing plane directly affects to the contour generation accuracy. The constructing contour from point cloud directly is a challenging task because there is no information of mesh topology, hence, no sequential order of points for contour generation. A search algorithm to find the most correlated points from skeletal points (reference) using the localized distance function is implemented. The experiment results show that our method reduces an average accuracy error for both wide and narrow point distributions by 15.57% to 35.20% and 4.28% to 12.78% respectively compared to the existina method.