Efficient Sampling of Disparity Space for Fast and Accurate Matching

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F07%3A03135193" target="_blank" >RIV/68407700:21230/07:03135193 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Efficient Sampling of Disparity Space for Fast and Accurate Matching

Original language description

A simple stereo matching algorithm is proposed that visits only a small fraction of disparity space in order to find a semi-dense disparity map. It works by growing from a small set of correspondence seeds. Unlike in known seedgrowing algorithms, it guarantees matching accuracy and correctness, even in the presence of repetitive patterns. This success is based on the fact it solves a global optimization task. The algorithm can recover from wrong initial seeds to the extent they can even be random. The quality of correspondence seeds influences computing time, not the quality of the final disparity map. We show that the proposed algorithm achieves similar results as an exhaustive disparity space search but it is two orders of magnitude faster. This is very unlike the existing growing algorithms which are fast but erroneous.

Czech name

Efficient Sampling of Disparity Space for Fast and Accurate Matching

Czech description

A simple stereo matching algorithm is proposed that visits only a small fraction of disparity space in order to find a semi-dense disparity map. It works by growing from a small set of correspondence seeds. Unlike in known seedgrowing algorithms, it guarantees matching accuracy and correctness, even in the presence of repetitive patterns. This success is based on the fact it solves a global optimization task. The algorithm can recover from wrong initial seeds to the extent they can even be random. The quality of correspondence seeds influences computing time, not the quality of the final disparity map. We show that the proposed algorithm achieves similar results as an exhaustive disparity space search but it is two orders of magnitude faster. This is very unlike the existing growing algorithms which are fast but erroneous.

Type

D - Article in proceedings

CEP classification

JD - Use of computers, robotics and its application

OECD FORD branch

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Project

<a href="/en/project/1ET101210406" target="_blank" >1ET101210406: Automatic 3D Virtual Model Builder from Photographs</a><br>

Continuities

R - Projekt Ramcoveho programu EK

Publication year

2007

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

CVPR 2007: Proceedings of the Computer Vision and Pattern Recognition conference

ISBN

1-4244-1180-7

ISSN

1053-587X

e-ISSN

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Number of pages

8

Pages from-to

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Publisher name

IEEE Computer Society

Place of publication

Los Alamitos

Event location

Minneapolis

Event date

Jun 18, 2007

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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