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Stereo Matching using Multi-resolution Images on CUDA

by Sudhakar Sah, Nikhil Jotwani
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 56 - Number 12
Year of Publication: 2012
Authors: Sudhakar Sah, Nikhil Jotwani
10.5120/8947-3119

Sudhakar Sah, Nikhil Jotwani . Stereo Matching using Multi-resolution Images on CUDA. International Journal of Computer Applications. 56, 12 ( October 2012), 47-55. DOI=10.5120/8947-3119

@article{ 10.5120/8947-3119,
author = { Sudhakar Sah, Nikhil Jotwani },
title = { Stereo Matching using Multi-resolution Images on CUDA },
journal = { International Journal of Computer Applications },
issue_date = { October 2012 },
volume = { 56 },
number = { 12 },
month = { October },
year = { 2012 },
issn = { 0975-8887 },
pages = { 47-55 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume56/number12/8947-3119/ },
doi = { 10.5120/8947-3119 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:58:41.570954+05:30
%A Sudhakar Sah
%A Nikhil Jotwani
%T Stereo Matching using Multi-resolution Images on CUDA
%J International Journal of Computer Applications
%@ 0975-8887
%V 56
%N 12
%P 47-55
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Stereo matching technique is used to estimate the depth of objects in an image acquired from real time scenes. The basic algorithm is not very complex but is computationally exhaustive and hinders its usage for real time applications. However, this algorithm is highly data parallel and it highly suitable for execution on GPGPU (General-purpose graphical processing units). In this paper, we are proposing the parallel implementation of the fast stereo matching algorithm based on correlation of multi-resolution images using CUDA (Compute Unified Device Architecture). The performance of this implementation is faster than most of the software implementations of this method and comparable with FPGA implementation and few other optimized methods mentioned in the references. This enables the real time usage of stereo matching method. We have also provided performance comparison and results for different methods of stereo matching on CUDA. The paper concludes with analysis of results and the reasons of the performance variations. We have also given qualitative image data for comparison of accuracy of different stereo correspondence methods.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Correlation Multi-Resolution images CUDA Stereo matching

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