標題: | Neural-network-based adaptive hybrid-reflectance model for 3-D surface reconstruction |
作者: | Lin, CT Cheng, WC Liang, SF 生物科技學系 資訊工程學系 電控工程研究所 Department of Biological Science and Technology Department of Computer Science Institute of Electrical and Control Engineering |
關鍵字: | enforcing integrability;Lambertian model;neural network;reflectance model;shape from shading;surface normal |
公開日期: | 1-十一月-2005 |
摘要: | This paper proposes a novel neural-network-based adaptive hybrid-reflectance three-dimensional (3-D) surface reconstruction model. The neural network automatically 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 are applied to enforce integrability when reconstructing 3-D objects. Facial images and images of other general objects were used to test the proposed approach. The experimental results demonstrate that the proposed neural-network-based adaptive hybrid-reflectance model can be successfully applied to objects generally and perform 3-D surface reconstruction better than some existing approaches. |
URI: | http://dx.doi.org/10.1109/TNN.2005.853333 http://hdl.handle.net/11536/13118 |
ISSN: | 1045-9227 |
DOI: | 10.1109/TNN.2005.853333 |
期刊: | IEEE TRANSACTIONS ON NEURAL NETWORKS |
Volume: | 16 |
Issue: | 6 |
起始頁: | 1601 |
結束頁: | 1615 |
顯示於類別: | 期刊論文 |