Fourier transform based methods for height from gradients

This paper presents a class of Fourier transform based approaches for height from gradients (HFG) problem, which is to reconstruct the 3D surface height of an object from its gradients. The HFG problem results from quite a few research areas in computer vision such as shape from shading, photometric stereo, shape from texture, shape from contours and so on. The proposed methods have some distinct advantages. Firstly, the derivation process of the algorithms has generality, and can be used for more functional dealing with additional constraints. Secondly, they are noniterative so that boundary conditions are not needed. In addition, their robustness to noisy gradient estimates can be improved by choosing associated weighting parameters. Experimental results using synthetic and real images are also presented.