A first-order optimization method for learning to reconstruct opacity in computational imaging

Unknown self-occlusion in a scene with opaque objects causes the multiview reconstruction problem to become ill-posed and nonlinear. In this report we describe a scalable nonlinear optimization method for simultaneously reconstructing the object and occlusion. The approach uses a simple learning stage which forms hypotheses for scene opacity given locally-optimal reconstruction estimates. This is combined with a first-order projected-gradient method for imposing physical consistency. Results are demonstrated for simulated examples.

A first-order optimization method for learning to reconstruct opacity in computational imaging.pdf