Mobile-based 3D modeling : An indepth evaluation for the application to maintenance and supervision

Abstract: Indoor environment modeling has become a relevant topic in several applications fields including Augmented, Virtual and Mixed Reality. Furthermore, with the Digital Transformation, many industries have moved toward this technology trying to generate detailed models of an environment allowing the viewers to navigate through it or mapping surfaces to insert virtual elements in a real scene. Therefore, this Thesis project has been conducted with the purpose to review well- established deterministic methods for 3D scene reconstruction and researching the state- of- the- art, such as machine learning- based approaches, and a possible implementation on mobile devices. Initially, we focused on the well- established methods such as Structure from Motion (SfM) that use photogrammetry to estimate camera poses and depth using only RGB images. Lastly, the research has been centered on the most innovative methods that make use of machine learning to predict depth maps and camera poses from a video stream. Most of the methods reviewed are completely unsupervised and are based on a combination of two subnetwork, the disparity network (DispNet) for the depth estimation and pose network (PoseNet) for camera pose estimation. Despite the fact that the results in outdoor application show high quality depth map and and reliable odometry, there are still some limitations for the deployment of this technology in indoor environment. Overall, the results are promising.