  This paper proposes a novel neural-network-based adaptive hybrid-reflectance
three-dimensional (3-D) surface reconstruction model. The neural network
combines the diffuse and specular components into a hybrid model. The proposed
model considers the characteristics of each point and the variant albedo to
prevent the reconstructed surface from being distorted. The neural network
inputs are the pixel values of the two-dimensional images to be reconstructed.
The normal vectors of the surface can then be obtained from the output of the
neural network after supervised learning, where the illuminant direction does
not have to be known in advance. Finally, the obtained normal vectors can be
applied to integration method when reconstructing 3-D objects. Facial images
were used for training in the proposed approach
