Alternating optimization for lambertian photometric stereo model with unknown lighting directions
Abstract
Photometric stereo is a technique of surface reconstruction using several object images made with a fixed camera position and varying illumination directions. Reconstructed surfaces can have complex reflecting properties which are unknown a priori and often simplified by Lambertian model (reflecting light uniformly in all directions). Such simplification leads to certain inaccuracy of reconstruction but in most cases is sufficient to obtain general object relief important for further recognition. Not only surface properties but also lighting sources utilized for each image acquisition can be very complex for modeling, or even unknown. Our work demonstrates how to find surface normals from Lambertian photometric stereo model using color images made with a priori unknown lighting directions. Evaluation of model components is based on an alternating optimization approach.