An adaptive mesh model for 3D reconstruction from unorganized data points

Abstract

Reconstructing exact topology mesh from data points is one of the most important tasks in the fields of industrial CAD/CAE/CAM, computational vision and reverse engineering. In this paper, a deflation algorithm that integrates an adaptive mesh and physical constraint model is presented for the 3D reconstruction of geometric-closed shape (genus 0) from unorganized data points. First, an initial mesh is formed using the Delaunay algorithm. Second, an asymptotic deforming performance is accomplished to deflate initial mesh towards the local concave boundary step-by-step. In this phase, a physical constraint model of coupled particle systems based on particle dynamics and Newtonian law of motion is constructed, and the model dynamically controls mesh deformation as a behavior constraint. To guarantee that the resultant mesh is homeomorphous to the original surface of data points, a continuously deforming mechanism, visibility cone and collision-detecting criterions are designed. At last, experimental results in reverse engineering which supports the usefulness of this method for reconstruction .