New constraints on data-closeness and needle map consistency for shape-from-shading

This paper makes two contributions to the problem of needle-map recovery us ing shape-from-shading. First, we provide a geometric update procedure whic h allows the image irradiance equation to be satisfied as a hard constraint . This not only improves the data closeness of the recovered needle-map, bu t also removes the necessity for extensive parameter tuning. Second, we exp loit the improved ease of control of the new shape-from-shading process to investigate various types of needle-map consistency constraint. The first s et of constraints are based on needle-map smoothness. The second avenue of investigation is to use curvature information to impose topographic constra ints. Third, we explore ways in which the needle-map is recovered so as to be consistent with the image gradient field. In each case we explore a vari ety of robust error measures and consistency weighting schemes that can be used to impose the desired constraints on the recovered needle-map. We prov ide an experimental assessment of the new shape-from-shading framework on b oth real world images and synthetic images with known ground truth surface normals. The main conclusion drawn from our analysis is that the data-close ness constraint improves the efficiency of shape-from-shading and that both the topographic and gradient consistency constraints improve the fidelity of the recovered needle-map.