Shape reconstruction from multiple RGB-D point cloud registration

The objective of this work is to present an object 3D reconstruction method using the point color information. The object 3D reconstruction is performed by combining point clouds obtained from different viewpoints using two cameras and a structured light projector. 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 (Iterative Closest Point) 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 an ICP that uses statistics to generate a dynamic distance and color threshold on the distance allowed between closest points is proposed and implemented. This approach allows subset matches, instead of matching all points from the point clouds. The surface reconstruction is performed using Marching Cubes and a consensus surface algorithm with signed distance compensates point cloud errors. In this paper the performance of the proposed method is analyzed and compared with the classical ICP.