Monocular albedo reconstruction

Reconstructing objects from monocular input video data is a challenging task. Since cues about shading and illumination are ambiguous from a single viewpoint, simultaneously distinguishing between material properties, texture, and illumination is infeasible. To properly reconstruct scene illumination a model of the object surface is mandatory. As most 3D surface reconstruction, surface refinement, and illumination reconstruction techniques rely on a diffuse object surface model, we propose a technique to robustly reconstruct surface albedo from monocular input video data and a 3D mesh representation of an object. For all frames of the video sequence we collect color samples for visible vertices under varying illumination directions and correct for possible ambient occlusion using mesh geometry information. We then use a two-staged clustering approach to recover the most plausible surface albedo for every mesh vertex. The presented approach does not require any a-priori illumination model and is evaluated using synthetic as well as real-world test sequences.