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Cited by 217

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Two-View Geometry Estimation Unaffected by a Dominant Plane

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Chum, O., Werner, T., Matas, J.·2005·2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)

Abstract

A RANSAC-based algorithm for robust estimation of epipolar geometry from point correspondences in the possible presence of a dominant scene plane is presented. The algorithm handles scenes with (i) all points in a single plane, (ii) majority of points in a single plane and the rest off the plane, (iii) no dominant plane. It is not required to know a priori which of the cases (i)-(iii) occurs. The algorithm exploits a theorem we proved, that if five or more of seven correspondences are related by a homography then there is an epipolar geometry consistent with the seven-tuple as well as with all correspondences related by the homography. This means that a seven point sample consisting of two outliers and five inliers lying in a dominant plane produces an epipolar geometry which is wrong and yet consistent with a high number of correspondences. The theorem explains why RANSAC often fails to estimate epipolar geometry in the presence of a dominant plane. Rather surprisingly, the theorem also implies that RANSAC-based homography estimation is faster when drawing nonminimal samples of seven correspondences than minimal samples of four correspondences.

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Metadata

Authors
Chum, O., Werner, T., & Matas, J.
Venue
2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
Publisher
IEEE
Published
2005-07-27
Volume
1
Pages
772-779
ISSN
1063-6919 (ISSN-L)
OpenAlex
W2099927035
Cited by
217
References
3
FWCI
6.51
Type
conference paper
Language
en
Open access
green

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CoChat. (2026). Citation check: Two-View Geometry Estimation Unaffected by a Dominant Plane. https://app.cochat.ai/papers/10.1109/cvpr.2005.354
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Chum, O., Werner, T., & Matas, J. (2005). Two-View Geometry Estimation Unaffected by a Dominant Plane. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). https://doi.org/10.1109/cvpr.2005.354

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