Efficient Poisson-Based Surface Reconstruction of 3D Model from a Non-homogenous Sparse Point Cloud

Poisson surface reconstruction is applied as an efficient technique to create a watertight surface from oriented point samples acquired with 3D range scanner or dense multi-view stereopsis. With non-homogenously distributed noisy sparse point cloud, Poisson surface reconstruction suffers from the problems of either over-smoothness, or large area of unrecognizable reconstructed surface. We present a novel three-step framework to provide a 3D mesh which better approximates the real surface of the object based on an iterative energy minimization process. The experimental results show the feasibility of the proposed approach on real image datasets.