Detail-Aware Uncalibrated Photometric Stereo

Photometric stereo is the problem of jointly inferring the 3D reconstruction, reflectance, lighting and specularities of an object from a set of visual signals. Recently, some variational, uncalibrated, unsupervised and unified formulations have provided robust solutions to the problem while reducing the prior knowledge about the shape geometry or the lighting conditions. Unfortunately, these approaches cannot still produce solutions with an ample variety of details in the shape. That is mainly due to the non-convex and non-linear nature of the problem which requires the best initialization as possible. In this context, we propose a fully interpretable formulation that combines a physically-aware image formation model under perspective projection with a minimal detail-aware initialization and that it can handle general lighting. As a result, our formulation can consider multiple scenarios composed of unknown complex geometries and lighting patterns. Experimental results on challenging synthetic and real datasets show the effectiveness of our approach to capture more fine details, outperforming state-of-the-art techniques in terms of 3D reconstruction.