An End-to-End Approach to Reconstructing 3D Model From Image Set

Large-scale 3D reconstruction from imagery has received much attention from the computer vision community. However, recovering 3D structures from 2D images is a notoriously complex process that requires expertise with often limited results. This paper presents an end-to-end 3D reconstruction system that can produce high-quality 3D models from a set of unordered image collections. Our workflow is a typical 3D reconstruction architecture that consists of structure from motion (SFM), multi-view stereo (MVS), and surface reconstruction, and can automatically recover desirable 3D models without any interactive operations. Finally, a comprehensive experiment is conducted on several benchmark datasets to assess the presented system. Experimental results show that the presented system achieves significant improvements in reconstruction accuracy and completeness over the existing state-of-the-art approach.