UCLA

Shape from Polarization (SfP) recovers an object's shape from polarized photographs of the scene. In previous works, the SfP algorithms use idealized physical equations to recover the shape. These previous approaches are error-prone when real-world conditions deviate from the idealized physics. In this thesis, we propose a physics-based neural network to address the SfP problem. Our algorithm fuses deep learning with synthetic renderings (derived from physics) to exceed the quality of all previous SfP methods. A two-stage encoder is used to resolve the longstanding problem of ambiguities. Our results of surface normal recovery are an improvement upon methods that utilize physics-based solutions alone.