BMVC 2004, Kingston, 7th-9th Sept, 2004

Reconstructing Relief Surfaces
G. Vogiatzis (University of Cambridge), P. Torr (Oxford Brookes
University), S. M. Seitz (University of Washington, USA) and R.Cipolla
(University of Cambridge)

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.
(pdf article)