Parameter Extraction and Performance Analysis of 3D Surface Reconstruction Techniques

Digital image-based 3D surface reconstruction is a streamlined and proper means of studying the features of the object being modelled. The generation of true 3D content is a very crucial step in any 3D system. A methodology to reconstruct a 3D surface of objects from a set of digital images is presented in this paper. It is simple, robust, and can be freely used for the construction of 3D surfaces from images. Digital images are taken as input to generate sparse and dense point clouds in 3D space from the detected and matched features. Poisson Surface, Ball Pivoting, and Alpha shape reconstruction algorithms have been used to reconstruct photo-realistic surfaces. Various parameters of these algorithms that are critical to the quality of reconstruction are identified and the effect of these parameters with varying values is studied. The results presented in this study will give readers an insight into the behaviour of various algorithmic parameters with computation time and fineness of reconstruction.