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A Neural Network for Simultaneously Reconstructing Transparent and Opaque Surfaces

  • Mohamad Ivan Fanany
  • Itsuo Kumazawa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4142)

Abstract

This paper presents a neural network (NN) to recover three-dimensional (3D) shape of an object from its multiple view images. The object may contain non-overlapping transparent and opaque surfaces. The challenge is to simultaneously reconstruct the transparent and opaque surfaces given only a limited number of views. By minimizing the pixel error between the output images of this NN and teacher images, we want to refine vertices position of an initial 3D polyhedron model to approximate the true shape of the object. For that purpose, we incorporate a ray tracing formulation into our NN’s mapping and learning. At the implementation stage, we develop a practical regularization learning method using texture mapping instead of ray tracing. By choosing an appropriate regularization parameter and optimizing using hierarchical learning and annealing strategies, our NN gives more approximate shape.

Keywords

Texture Mapping Vertex Position True Shape Transparent Surface Transparent Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mohamad Ivan Fanany
    • 1
  • Itsuo Kumazawa
    • 1
  1. 1.Imaging Science and EngineeringTokyo Institute of Technology 

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