A Novel Factor Graph-Based Optimization Technique for Stereo Correspondence Estimation

TL;DR

This paper introduces a new factor graph-based optimization method for stereo correspondence estimation that adapts neighborhood structures based on local scene features, improving disparity accuracy over existing methods.

Contribution

The paper presents a novel factor graph model with variable neighborhood structures for disparity estimation, enhancing accuracy in complex scenes compared to prior fixed-neighborhood MRF approaches.

Findings

Achieved higher disparity accuracy on Middlebury datasets.

Outperformed recent state-of-the-art algorithms.

Flexible neighborhood structure improves robustness in heterogeneous scenes.

Abstract

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.