Multi-view Photometric Stereo by Example

Abstract

We present a novel multi-view photometric stereo technique that recovers the surface of texture less objects with unknown BRDF and lighting. The camera and light positions are allowed to vary freely and change in each image. We exploit orientation consistency between the target and an example object to develop a consistency measure. Motivated by the fact that normals can be recovered more reliably than depth, we represent our surface as both a depth map and a normal map. These maps are jointly optimized and allow us to formulate constraints on depth that take surface orientation into account. Our technique does not require the visual hull or stereo reconstructions for bootstrapping and solely exploits image intensities without the need for radiometric camera calibration. We present results on real objects with varying degree of specularity and show that these can be used to create globally consistent models from multiple views.