# Accurate and Fast Convergent Initial-Value Belief Propagation for Stereo Matching

## TL;DR

This paper introduces a new belief propagation algorithm for stereo matching that improves accuracy and convergence speed.

## Contribution

The novel initial-value belief propagation algorithm and self-adapting dissimilarity data term enhance BP performance.

## Key findings

- The IVBP algorithm reduces iterations and improves convergence speed and accuracy.
- The SDDT improves data term accuracy by incorporating gradient-based measures.
- The method achieves better edge-preserving smoothing on Middlebury datasets.

## Abstract

The belief propagation (BP) algorithm has some limitations, including ambiguous edges and textureless regions, and slow convergence speed. To address these problems, we present a novel algorithm that intrinsically improves both the accuracy and the convergence speed of BP. First, traditional BP generally consumes time due to numerous iterations. To reduce the number of iterations, inspired by the crucial importance of the initial value in nonlinear problems, a novel initial-value belief propagation (IVBP) algorithm is presented, which can greatly improve both convergence speed and accuracy. Second, .the majority of the existing research on BP concentrates on the smoothness term or other energy terms, neglecting the significance of the data term. In this study, a self-adapting dissimilarity data term (SDDT) is presented to improve the accuracy of the data term, which incorporates an additional gradient-based measure into the traditional data term, with the weight determined by the robust measure-based control function. Finally, this study explores the effective combination of local methods and global methods. The experimental results have demonstrated that our method performs well compared with the state-of-the-art BP and simultaneously holds better edge-preserving smoothing effects with fast convergence speed in the Middlebury and new 2014 Middlebury datasets.