Multi-view NRSfM: Affordable Setup for High-Fidelity 3D Reconstruction

Abstract

Triangulating a point in 3D space should only require two corresponding camera projections. However in practice, expensive multi-view setups -- involving tens sometimes hundreds of cameras -- are required to obtain the high fidelity 3D reconstructions necessary for many modern applications. In this thesis, we argue that similar fidelity can be obtained using as little as two cameras by breaking the tenet of rigidity which is central to much of modern multi-view geometry. Our approach instead leverages recent advances in Non-Rigid Structure from Motion (NRSfM) using neural shape priors while also enforcing multi-view equivariance. We show how our method can achieve comparable fidelity to expensive multi-view rigs using only two physical camera views.