3D reconstruction of point clouds using multi-view orthographic projections

Abstract

A method to reconstruct 3D point clouds using multi-view orthographic projections is examined. Point clouds are generated by means of a stochastic process. This stochastic process is designed to generate point clouds that mimic microcalcification formation in breast tissue. Point clouds are generated using a Gibbs sampler algorithm. Orthographic projections of point clouds from any desired orientation are generated. Volumetric intersection method is employed to perform the reconstruction from these orthographic projections. The reconstruction may yield erroneous reconstructed points. The types of these erroneous points are analyzed along with their causes and a performance measure based on linear combination is devised. Experiments have been designed to investigate the effect of the number of projections and the number of points to the performance of reconstruction. Increasing the number of projections and decreasing the number of points resulted in better reconstructions that are more similar to the original point clouds. However, it is observed that reconstructions do not improve considerably upon increasing the number of projections after some number. This method of reconstruction serves well to find locations of original points.