3D Reconstruction of Large Point Clouds with a New Point Correspondence Algorithm

Abstract

The objective of this work is to perform the 3D reconstruction combining cloud points obtained from different viewpoints using structured light. The point cloud is simplified to reduce the computational time. The main task is the point cloud registration algorithm that matches two point clouds. A well known algorithm for point cloud registration is the ICP that determines the rotation and translation that when applied to one of the point clouds, place both point clouds in accordance. The ICP algorithm executes iteratively two main steps: point correspondence determination and registration algorithm. The point correspondence determination is a module that if not executed properly can make the ICP to converge to a local minimum. To overcome such drawback, it is proposed in this work an ICP that uses statistics to generate a dynamic distance threshold on the distance allowed between closest points. Instead of matching all points from the data set, this technique matches subset-subset points.