Tsukuba
Venus
The belief propagation based stereo approach approximates the minimum energy solution on graphical models such as Markov Chains, or Markov Random Field (MRF) of disparities. Our approach exploits a symmetric Cyclopean matching model, which accounts for visibility conditions, to construct epipolar profiles which are close to the human perception. Unlike traditional asymmetric matching models, this model can construct disparity maps with respect to the left, right or Cyclopean reference frame, as well as a Cyclopean image of a 3D scene depicted in a stereo pair, simultaneously.
We focused on both one-dimensional (1D), and two-dimensional (2D) belief propagation. 1D belief propagation has the advantage of fast computation, and low memory usage, but suffers matching errors due to the lack of vertical information. 2D belief propagation is more memory intensive, and has slower computation speed, but it can achieve high quality results using the powerful 2D message passing, where matching information is passed around the MRF, and decisions are made using all the image information.
The results of symmetric 2D belief propagation are shown.