  Dense disparities among multiple views is essential for estimating the 3D
architecture of a scene based on the geometrical relationship among the scene
and the views or cameras. Scenes with larger extents of heterogeneous textures,
differing scene illumination among the multiple views and with occluding
objects affect the accuracy of the estimated disparities. Markov random fields
(MRF) based methods for disparity estimation address these limitations using
spatial dependencies among the observations and among the disparity estimates.
These methods, however, are limited by spatially fixed and smaller neighborhood
systems or cliques. In this work, we present a new factor graph-based
probabilistic graphical model for disparity estimation that allows a larger and
a spatially variable neighborhood structure determined based on the local scene
characteristics. We evaluated our method using the Middlebury benchmark stereo
datasets and the Middlebury evaluation dataset version 3.0 and compared its
performance with recent state-of-the-art disparity estimation algorithms. The
new factor graph-based method provided disparity estimates with higher accuracy
when compared to the recent non-learning- and learning-based disparity
estimation algorithms. In addition to disparity estimation, our factor graph
formulation can be useful for obtaining maximum a posteriori solution to
optimization problems with complex and variable dependency structures as well
as for other dense estimation problems such as optical flow estimation.
