Iterative closest point based 3D object reconstruction using RGB-D acquisition devices

This paper introduces, and verifies the applicability of, a practical algorithm for creating 3D points clouds resembling objects, based on multiple RGB-D frames having been taken from different viewpoints, by the Kinect 2 camera. The experimental set-up is described, along with a certain variant of the process referred to as iterative closest point, being utilized for the latter purpose. Moreover, in order to achieve reliable and realistic representations of the objects, the noise involved in the initial result of the reconstruction procedure is removed through employing a high-pass filter mask, being aimed at detecting and excluding the outliers. The proposed method, based on the experiments whose results are reported in the paper, is computationally less costly than the relevant alternatives suggested in the literature heretofore, which is one of its fundamental contributions when dealing with real-time scenarios. The strengths and weaknesses of the proposed algorithm are discussed according to the results, where objects of various sorts, i.e. with different colors and types of materials and surface, are taken into account and investigated as case-studies.