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

arXiv: https://arxiv.org/abs/2109.11077 · HTML: https://mlsanity.com/paper/2109.11077/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 78 references that are in the map.
- First posted on arXiv 22 Sep 2021.

## Builds on (most cited first, 12 of 12)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Pyramid Stereo Matching Network](https://mlsanity.com/paper/1803.08669/) (2018)
- [End-to-End Learning of Geometry and Context for Deep Stereo Regression](https://mlsanity.com/paper/1703.04309/) (2017)
- [Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches](https://mlsanity.com/paper/1510.05970/) (2015)
- [Loopy Belief Propagation for Approximate Inference: An Empirical Study](https://mlsanity.com/paper/1301.6725/) (2013)
- [Stereo Matching With Color-Weighted Correlation, Hierarchical Belief Propagation And Occlusion Handling](https://mlsanity.com/paper/1708.07987/) (2017)
- [Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems](https://mlsanity.com/paper/2003.06258/) (2020)
- [FC-DCNN: A densely connected neural network for stereo estimation](https://mlsanity.com/paper/2010.06950/) (2020)
- [Deep-Learning Assisted High-Resolution Binocular Stereo Depth Reconstruction](https://mlsanity.com/paper/1912.05012/) (2019)
- [Video Semantic Object Segmentation by Self-Adaptation of DCNN](https://mlsanity.com/paper/1711.08180/) (2017)
- [Cascaded multi-scale and multi-dimension convolutional neural network for stereo matching](https://mlsanity.com/paper/1803.09437/) (2018)
- [MTStereo 2.0: improved accuracy of stereo depth estimation withMax-trees](https://mlsanity.com/paper/2006.15373/) (2020)

## Built on by (most cited first, 1 of 1)

- [Multi-Resolution Factor Graph Based Stereo Correspondence Algorithm](https://mlsanity.com/paper/2202.01309/) (2022)

## Abstract (arXiv)

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.