Multi-Resolution Factor Graph Based Stereo Correspondence Algorithm
This work provides a more accurate stereo matching method for computer vision applications, but it is incremental as it builds on existing factor graph models.
The paper tackled the problem of estimating dense depth maps from stereo images by addressing challenges like homogeneous regions and occlusions, resulting in a multi-resolution factor graph-based algorithm (MR-FGS) that improved accuracy and depth boundary contrast without needing post-processing.
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.