Two Simple Yet Effective Strategies for Avoiding Over-Smoothing in SFS Problem

Minimization techniques are widely used for retrieving a 3D surface starting from a single shaded image i.e., for solving the shape from shading problem. Such techniques are based on the assumption that expected surface to be retrieved coincides with the one that minimize a properly developed functional, consisting of several contributions. Among the possible contributes defining the functional, the so called “smoothness constraint” is always used since it guides the convergence of the minimization process towards a more accurate solution. Unfortunately, in areas where actually brightness changes rapidly, it also introduces an undesired over-smoothing effect. The present work proposes two simple yet effective strategies for avoiding the typical over-smoothing effect, with regards to the image regions in which this effect is particularly undesired (e.g., areas where surface details are to be preserved in the reconstruction). Tested against a set of case studies the strategies prove to outperform traditional SFS-based methods.