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Shape from Photographs: A Multi-view Stereo Pipeline

  • Carlos Hernández
  • George Vogiatzis
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 285)

Abstract

Acquiring 3D shape from images is a classic problem in Computer Vision occupying researchers for at least 20 years. Only recently however have these ideas matured enough to provide highly accurate results. We present a complete algorithm to reconstruct 3D objects from images using the stereo correspondence cue. The technique can be described as a pipeline of four basic building blocks: camera calibration, image segmentation, photo-consistency estimation from images, and surface extraction from photo-consistency. In this Chapter we will put more emphasis on the latter two: namely how to extract geometric information from a set of photographs without explicit camera visibility, and how to combine different geometry estimates in an optimal way.

Keywords

Markov Random Field Camera Calibration Deformable Model Normalize Cross Correlation Visual Hull 
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 2010

Authors and Affiliations

  • Carlos Hernández
    • 1
  • George Vogiatzis
    • 2
  1. 1.Toshiba Research CambridgeUK
  2. 2.Aston UniversityBirminghamUK

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