A taxonomy and evaluation of dense two-frame stereo correspondence algorithms

A taxonomy and evaluation of dense two-frame stereo correspondence algorithms (2002)

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by Daniel Scharstein , Richard Szeliski

Venue:	International Journal of Computer Vision
Citations:	709 - 18 self

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Datum	Value	Source
TITLE	A taxonomy and evaluation of dense two-frame stereo correspondence algorithms	INFERENCE
AUTHOR NAME	Daniel Scharstein	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Mathematics and Computer Science, Middlebury College	SVM HeaderParse 0.2
AUTHOR ADDR	Middlebury, VT 05753, USA	SVM HeaderParse 0.2
AUTHOR NAME	Richard Szeliski	SVM HeaderParse 0.2
AUTHOR AFFIL	Microsoft Research, Microsoft Corporation, Redmond	SVM HeaderParse 0.2
AUTHOR ADDR	WA 98052, USA	SVM HeaderParse 0.2
ABSTRACT	Abstract. Stereo matching is one of the most active research areas in computer vision. While a large number of algorithms for stereo correspondence have been developed, relatively little work has been done on characterizing their performance. In this paper, we present a taxonomy of dense, two-frame stereo methods. Our taxonomy is designed to assess the different components and design decisions made in individual stereo algorithms. Using this taxonomy, we compare existing stereo methods and present experiments evaluating the performance of many different variants. In order to establish a common software platform and a collection of data sets for easy evaluation, we have designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can easily be extended to include new algorithms. We have also produced several new multi-frame stereo data sets with ground truth and are making both the code and data sets available on the Web. Finally, we include a comparative evaluation of a large set of today’s best-performing stereo algorithms.	SVM HeaderParse 0.2
YEAR	2002	INFERENCE
VENUE	International Journal of Computer Vision	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	7--42	INFERENCE
VOLUME	47	INFERENCE
CITATIONS	130 found	ParsCit 1.0