Neural Network Implementation of Image Rendering via Self-Calibration
Yi Ding*1, Yuji Iwahori*2, Tsuyoshi Nakamura*1, Lifeng He*3,
Robert J. Woodham*4, and Hidenori Itoh*1
*1Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya 466-8555, Japan
*2Dept. of Computer Science, Chubu University, 1200 Matsumoto-cho, Kasugai 487-8501, Japan
*3Faculty of Information Science and Technology, Aichi Prefectural University, 1522-3 Kumabari-Ibaragabasama, Nagakute, Aichi-gun, Aichi 480-1198, Japan
*4University of British Columbia, Vancouver, B.C. V6T 1Z4, Canada
This paper proposes a new approach for selfcalibration and color image rendering using Radial Basis Function (RBF) neural network. Most empirical approaches make use of a calibration object. Here, we require no calibration object to both shape recovery and color image rendering. The neural network learning data are obtained through the rotations of a target object. The approach can generate realistic virtual images without any calibration object which has the same reflectance properties as the target object. The proposed approach uses a neural network to obtain both surface orientation and albedo, and applies another neural network to generate virtual images for any viewpoint and any direction of light source. Experiments with real data are demonstrated.
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