In this article, a new methodology for color shape from shading (SFS) problem is proposed. The problem of color SFS refers to the well-known fact that most real objects usually contain mixtures of diffuse and specular color reflections and are affected by the multicolored interreflection under unknown reflectivity. In this article, these limitations are addressed, and a new color SFS methodology is proposed. The proposed approach focuses on two main parts. First, a generalized neural-based color reflectance model is developed. Second, an iterative recursive method is developed to reconstruct a multicolor 3D surface. Experimental results on synthetic-colored objects and real-colored objects were performed to demonstrate the performance of the proposed methodology.

It is known that most real surfaces usually are neither perfectly Lambertian model nor ideally specular model; rather, they are formed by the hybrid structure of these two models. This hybrid reflectance model still suffers from the noise, strong specular, and unknown reflectivity conditions. In this article, these limitations are addressed, and a new neural-based hybrid reflectance model is proposed. The goal of this method is to optimize a proper reflectance model by learning the weight and parameters of the hybrid structure of feedforward neural networks and radial basis function networks and to recover the 3D object shape by the shape from shading technique with this resulting model. Experimental results, including synthetic and real images, were performed to demonstrate the performance of the proposed reflectance model in the case of different specular effects and noise environments.