Enhanced 3D Shape Recovery Using the Neural-Based Hybrid Reflectance Model

Authors:
Siu-Yeung Cho;Tommy W. S. Chow
Affiliations:
Dept. of Electrical Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong;Dept. of Electrical Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong
Venue:
Neural Computation
Year:
2001

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Abstract

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.