  A dense depth-map of a scene at an arbitrary view orientation can be
estimated from dense view correspondences among multiple lower-dimensional
views of the scene. These low-dimensional view correspondences are dependent on
the geometrical relationship among the views and the scene. Determining dense
view correspondences is difficult in part due to presence of homogeneous
regions in the scene and due to presence of occluded regions and illumination
differences among the views. We present a new multi-resolution factor
graph-based stereo matching algorithm (MR-FGS) that utilizes both intra- and
inter-resolution dependencies among the views as well as among the disparity
estimates. The proposed framework allows exchange of information among multiple
resolutions of the correspondence problem and is useful for handling larger
homogeneous regions in a scene. The MR-FGS algorithm was evaluated
qualitatively and quantitatively using stereo pairs in the Middlebury stereo
benchmark dataset based on commonly used performance measures. When compared to
a recently developed factor graph model (FGS), the MR-FGS algorithm provided
more accurate disparity estimates without requiring the commonly used
post-processing procedure known as the left-right consistency check. The
multi-resolution dependency constraint within the factor-graph model
significantly improved contrast along depth boundaries in the MR-FGS generated
disparity maps.
