Reconstructing relief surfaces

George Vogiatzis*, Philip H.S. Torr, Steven M. Seitz, Roberto Cipolla

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This paper generalizes Markov Random Field (MRF) stereo methods to the generation of surface relief (height) fields rather than disparity or depth maps. This generalization enables the reconstruction of complete object models using the same algorithms that have been previously used to compute depth maps in binocular stereo. In contrast to traditional dense stereo where the parametrization is image based, here we advocate a parametrization by a height field over any base surface. In practice, the base surface is a coarse approximation to the true geometry, e.g., a bounding box, visual hull or triangulation of sparse correspondences, and is assigned or computed using other means. A dense set of sample points is defined on the base surface, each with a fixed normal direction and unknown height value. The estimation of heights for the sample points is achieved by a belief propagation technique. Our method provides a viewpoint independent smoothness constraint, a more compact parametrization and explicit handling of occlusions. We present experimental results on real scenes as well as a quantitative evaluation on an artificial scene.

Original languageEnglish
Pages (from-to)397-404
Number of pages8
JournalImage and Vision Computing
Volume26
Issue number3
DOIs
Publication statusPublished - 3 Mar 2008

Keywords

  • Belief propagation
  • MRF
  • Multi-view
  • Reconstruction
  • Stereo
  • Vision
  • Volumetric

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