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Recovering the shape and reflectance of non-Lambertian surfaces remains achallenging problem in computer vision since the view-dependent appearanceinvalidates traditional photo-consistency constraint. In this paper, weintroduce a novel concentric multi-spectral light field (CMSLF) design that isable to recover the shape and reflectance of surfaces with arbitrary materialin one shot. Our CMSLF system consists of an array of cameras arranged onconcentric circles where each ring captures a specific spectrum. Coupled with amulti-spectral ring light, we are able to sample viewpoint and lightingvariations in a single shot via spectral multiplexing. We further show thatsuch concentric camera/light setting results in a unique pattern of specularchanges across views that enables robust depth estimation. We formulate aphysical-based reflectance model on CMSLF to estimate depth and multi-spectralreflectance map without imposing any surface prior. Extensive synthetic andreal experiments show that our method outperforms state-of-the-art lightfield-based techniques, especially in non-Lambertian scenes.

Non-Lambertian Surface Shape and Reflectance Reconstruction Using Concentric Multi-Spectral Light Field