Comparison of Stereo Matching Algorithms for the Development of Disparity Map

TL;DR

This study compares six stereo matching algorithms and three cost functions to determine their effectiveness in creating accurate disparity maps from stereo images, highlighting the importance of matching function selection.

Contribution

It provides a comprehensive comparison of multiple stereo matching algorithms and cost functions, including a new proposed method, for improved disparity map accuracy.

Findings

Belief Propagation achieved over 95% accuracy in most cases.

Matching function choice significantly impacts disparity map quality.

Performance varies depending on image properties and calibration quality.

Abstract

Stereo Matching is one of the classical problems in computer vision for the extraction of 3D information but still controversial for accuracy and processing costs. The use of matching techniques and cost functions is crucial in the development of the disparity map. This paper presents a comparative study of six different stereo matching algorithms including Block Matching (BM), Block Matching with Dynamic Programming (BMDP), Belief Propagation (BP), Gradient Feature Matching (GF), Histogram of Oriented Gradient (HOG), and the proposed method. Also three cost functions namely Mean Squared Error (MSE), Sum of Absolute Differences (SAD), Normalized Cross-Correlation (NCC) were used and compared. The stereo images used in this study were from the Middlebury Stereo Datasets provided with perfect and imperfect calibrations. Results show that the selection of matching function is quite important and also depends on the images properties. Results showed that the BP algorithm in most cases provided better results getting accuracies over 95%.