A Neural Network Approach for Indirect Shape from Shading

Abstract

For the reason that the conventional illumination models are empirical and non-linear, the traditional shape from shading (SFS) methods with conventional illumination models are always divergent in the process of iteration and are difficult to initialize the parameters of the illumination model. To overcome these disadvantages, a new approach based on the neural network for indirect SFS is proposed in this paper. The new proposed approach applies a series of standard sphere pictures, in which the gradients of the sphere can be calculated, to train a neural network model. Then, the gradients of the reconstructed object pictures, which are taken in the similar circumstances as that of the standard sphere pictures, can be obtained from the network model. Finally, the height of the surface points can be calculated. The results show that the new proposed method is effective, accurate and convergent.