Multi-Resolution Factor Graph Based Stereo Correspondence Algorithm
Hanieh Shabanian, Madhusudhanan Balasubramanian

TL;DR
This paper introduces a multi-resolution factor graph-based stereo matching algorithm that improves depth estimation accuracy by leveraging intra- and inter-resolution dependencies, effectively handling homogeneous regions and occlusions.
Contribution
The novel MR-FGS algorithm integrates multi-resolution dependencies into a factor graph framework, enhancing disparity estimation without post-processing.
Findings
More accurate disparity estimates than previous models
Improved contrast along depth boundaries
Effective handling of homogeneous regions and occlusions
Abstract
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…
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Taxonomy
TopicsAdvanced Vision and Imaging · Satellite Image Processing and Photogrammetry · Advanced Image Processing Techniques
