Fully automatic 3D object reconstruction from multi-view range scan data

3D surface registration of two or more range scans is an important step in building a complete 3D model of an object. When the overlaps between multi-view scans are insufficient, it is highly compulsory to involve good initial alignment that typically requires some prior assumption such as pre-defined initial camera configuration or the use of landmarks. Specifically, this research attempts to address the problem of registering two or more range scans captured from the complex 3D objects which entail an extremely small amount of overlaps and where camera initialization or configuration is not known. This newly proposed algorithm consists of two steps of registration: coarse registration and fine registration. First, coarse registration involves two new techniques that can create an enclosed mesh model from a single range scan to extract reliable 3D surface features for initial alignment, Partial Artificial Heat Kernel Signature (PA-HKS) and Artificial Symmetric Volume Heat Kernel Signature (AS-HKS). Coarse registration is successfully achieved by estimation of the feature descriptors from the generated artificial 3D volume and by Heat Kernel Signature (HKS) from the artificial 3D mesh data, as an important attempt to correctly identify correspondences among the partial 3D range scan data. Secondly, fine registration involves a Modified Multi-view Iterative Contour Coherence (MM-ICC) algorithm. On the basis of the preliminary registration, the coarsely aligned partial range scan data is matched by the MM-ICC method in the fine registration step. This unique combination allows us to successfully handle multi-view range scan data with large out-of-plane rotation and limited overlaps between two adjacent views and without any camera information. The experimental results of a number of complex 3D objects clearly illustrate and validate the effectiveness and robustness of the proposed approach. To be more specific, the research outcomes are found to be highly successful even in case of 1-2% overlapping areas, whereas the previous studies require at least 45-50% of overlapping regions