Abstract

Light-field cameras have recently emerged as a powerfultool for one-shot passive 3D shape capture. However, ob-taining the shape of glossy objects like metals, plastics orceramics remains challenging, since standard Lambertian cues like photo-consistency cannot be easily applied. In thispaper, we derive a spatially-varying (SV)BRDF-invarianttheory for recovering 3D shape and reflectance from light-field cameras. Our key theoretical insight is a novel analy-sis of diffuse plus single-lobe SVBRDFs under a light-fieldsetup. We show that, although direct shape recovery is notpossible, an equation relating depths and normals can still be derived. Using this equation, we then propose using a polynomial (quadratic) shape prior to resolve the shape ambiguity. Once shape is estimated, we also recover the reflectance. We present extensive synthetic data on the entire MERL BRDF dataset, as well as a number of real examples to validate the theory, where we simultaneously recover shape and BRDFs from a single image taken with a Lytro Illum camera.