Neural-network-based adaptive hybrid-reflectance model for 3-D surface reconstruction

Chin Teng Lin, Wen Chang Cheng, Sheng Fu Liang

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1601-1615
Number of pages15
JournalIEEE Transactions on Neural Networks
Issue number6
Publication statusPublished - 2005 Nov

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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