Surface Reconstruction Techniques Using Neural Networks to Recover Noisy 3D Scenes (original) (raw)

Abstract

This paper presents a novel neural network approach to recovering of 3D surfaces from single gray scale images. The proposed neural network uses photometric stereo to estimate local surfaces orientation for surfaces at each point of the surface that was observed from same viewpoint but with different illumination direction in surfaces that follow the Lambertian reflection model. The parameters for the neural network are a 3x3 brightness patch with pixel values of the image and the light source direction. The light source direction of the surface is calculated using two different approaches. The first approach uses a mathematical method and the second one a neural network method. The images used to test the neural network were both synthetic and real images. Only synthetic images were used to compare the approaches mainly because the surface was known and the error could be calculated. The results show that the proposed neural network is able to recover the surface with a highly accurately estimate.

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Authors and Affiliations

  1. Centre for Computational Intelligence, School of Computing, De Montfort University, The Gateway, Leicester, LE1 9BH, UK
    David Elizondo, Shang-Ming Zhou & Charalambos Chrysostomou

Authors

  1. David Elizondo
  2. Shang-Ming Zhou
  3. Charalambos Chrysostomou

Editor information

Véra Kůrková Roman Neruda Jan Koutník

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© 2008 Springer-Verlag Berlin Heidelberg

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Elizondo, D., Zhou, SM., Chrysostomou, C. (2008). Surface Reconstruction Techniques Using Neural Networks to Recover Noisy 3D Scenes. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87536-9\_88

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