Experimental Validation of Photogrammetry based 3D Reconstruction Software

3D reconstruction is of interest to several fields. However, obtaining the 3D model is usually a time-consuming task that involves manual measurements and reproduction of the object using CAD software, which is not always feasible (e.g. for organic shapes). The necessity of quickly obtaining a dimensionally accurate 3D model of an object has led to the development of several reconstruction techniques, either vision based (with photogrammetry), using laser scanners, or a combination of the two. The contribution of this study is in the analysis of the performances of currently available 3D reconstruction frameworks with the aim of providing a guideline to novice users who may be unfamiliar with 3D reconstruction technologies. We evaluate various software packages on a synthetic dataset representing objects of various shapes and sizes. For comparison, we consider various metrics such as mean errors in the reconstructed cloud point and meshes and reconstruction time. Our results indicate that Colmap produces the best reconstruction.