Browsing by Subject "Multi-View Stereo"

Recently, 3D reconstruction has become a popular topic due to applications in Virtual Reality, Augmented Reality, and historical heritage preservation. Yet, high-quality reconstruction is not available to the general public because of the cost of laser scanners. The goal of this thesis is to make the democratization of 3D reconstruction closer with the use of photogrammetry (reconstruction from multi-view images.) However, current approaches are very slow and tend to oversmooth the geometry. Our method involves learning the scene via a neural representation by taking posed multi-view images as input. We note that state-of-the-art (SOTA) approaches rely on traditional Structure-from-Motion (SfM) algorithms to extract camera poses. We also observe that SfM can generate a coarsely correct mesh for the underlying object. Nevertheless, SOTA techniques start training the neural representation from a sphere. Therefore, we propose a novel initialization method that takes the mesh obtained from SfM and initializes the neural representation from it. We validate our approach through extensive experiments on a widely used multi-view stereo DTU dataset. We show that our method outperforms both traditional and SOTA neural techniques in terms of reconstruction quality. It manages to learn the underlying geometry and recover small details like cracks and dents. We also show that it speeds up convergence by 4 times. All the datasets, reconstructed meshes, and learned model weights are available at this link.