Deeper Understanding on Solution Ambiguity by Homography Decomposition

He, Liu; Häse, H.; Tokai, Shogo

doi:10.2316/p.2011.740-004

Cited by 3 publications

(3 citation statements)

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“…For example, if I = 10, i.e. 10 inlier and 5 outlier views are given, the outlier percentage is calculated as 1− 10 2 / 15 2 ≈ 0.57. In the figures, the horizontal axis reports the outlier ratio and the vertical one shows the mean angular error of the results.…”

Section: A Synthesized Testsmentioning

confidence: 99%

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Optimal Multi-View Surface Normal Estimation Using Affine Correspondences

Baráth

Eichhardt

Hajder

2019

IEEE Trans. on Image Process.

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An optimal, in the least squares sense, method is proposed to estimate surface normals in both stereo and multi-view cases. The proposed algorithm exploits exclusively photometric information via affine correspondences and estimates the normal for each correspondence independently. The normal is obtained as a root of a quartic polynomial. Therefore, the processing time is negligible. Eliminating the outliers, we propose a robust extension of the algorithm that combines maximum likelihood estimation and iteratively re-weighted least squares. The method has been validated on both synthetic and publicly available real-world datasets. It is superior to the state of the art in terms of accuracy and processing time. Besides, we demonstrate two possible applications: 1) using our algorithm as the seed-point generation step of patch-based multi-view stereo method, the obtained reconstruction is more accurate, and the error of the 3D points is reduced by 30% on average and 2) multiplane fitting becomes more accurate applied to the resulting oriented point cloud.

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Section: A Synthesized Testsmentioning

confidence: 99%

“…However, the decomposition itself is ambiguous as it was shown by several studies, e.g. in [10], and homography estimation cannot be done for each point correspondence independently. Thus, in general, these methods are applied to corresponding image regions supposing that the underlying surface patch is planar.…”

Section: Introductionmentioning

confidence: 99%

Optimal Multi-View Surface Normal Estimation Using Affine Correspondences

Baráth

Eichhardt

Hajder

2019

IEEE Trans. on Image Process.

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“…However, the decomposition itself is ambiguous as it was shown by several studies, e.g. in [56], and homography estimation cannot be done for each point correspondence independently. Thus, in general, these methods are applied to superpixel correspondences, i.e.…”

Section: Optimal Multi-view Surface Normal Estimationmentioning

confidence: 99%

Affine Correspondences and their Applications for Model Estimation

Baráth¹

2019

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Department of Algorithms And Their Applications Doctor of Philosophy Affine Correspondences and their Applications for Model Estimationby Dániel BARÁTH This work aims to solve sub-problems of two major fields in computer vision: minimal problems in two-or multi-view geometric model estimation and robust model fitting. Minimal solvers, i.e. algorithms solving estimation problems from a minimal sample of data points, are involved in most of the vision pipelines as an the engine of the applied robust method, e.g. RANSAC [1] and its recent variants. Vision pipelines, including calibration, structure-from-motion, image matching and retrieval, benefits from efficient minimal solvers which improve their performance upon. Given a minimal point correspondence set in two views, state-ofthe-art solvers, with a few exceptions, use them solely through their coordinates. Nevertheless, as it will be demonstrated in this thesis, exploiting affine correspondences which encode higher-order geometric information leads to methods superior to the state-of-the-art in terms of stability and the number of data points required. Methods will be proposed for surface normal, homography, epipolar geometry, and focal length estimation.The second major part of this work focuses on robust model fitting which is also a significant part of vision tasks. The base problem is to fit a single or more model instances, e.g. planes to a 3D point cloud or fundamental matrix to point correspondences, interpreting the input whilst it is contaminated by noise and contains outliers. We consider outliers as points not belonging to any desired model instance. First, a method is proposed to distinguish inliers and outliers in a set of correspondences without necessarily assuming an underlying model. Then a new local optimization step is proposed for locally optimized RANSAC (LO-RANSAC) outperforming its state-of-the-art variants. Finally, we focus on multi-homography, then general multi-class multi-instance, fitting -the problem of interpreting the input data as a mixture of noisy observations originating from multiple instances of multiple classes. The methods proposed in this work were validated both on synthesized and publicly available real world datasets.

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