Multiple Models Fitting as a Set Coverage Problem

Magri, Luca; Fusiello, Andrea

doi:10.1109/cvpr.2016.361

Cited by 58 publications

(72 citation statements)

References 33 publications

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“…Table 2 shows the mean and median misclassification errors on all 19 image pairs of the AdelaideRMF dataset. The competitor methods are T-Linkage [11], J-Linkage [24], RPA [10], SA-RCM [20], Greedy-RansaCov [12] and ILP-RansaCov [12]. Multi-H significantly outperforms all published methods.…”

Section: Comparison With Multi-homography Fitting Techniquesmentioning

confidence: 96%

Multi-H: Efficient recovery of tangent planes in stereo images

Baráth¹,

Matas²,

Hajder³

2016

Procedings of the British Machine Vision Conference 2016

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Multi-H -an efficient method for the recovery of the tangent planes of a set of point correspondences satisfying the epipolar constraint is proposed. The problem is formulated as a search for a labeling minimizing an energy that includes a data and spatial regularization terms. The number of planes is controlled by a combination of MeanShift and α-expansion.Experiments on the fountain-P11 3D dataset show that Multi-H provides highly accurate tangent plane estimates. It also outperforms all state-of-the-art techniques for multihomography estimation on the publicly available AdelaideRMF dataset. Since Multi-H achieves nearly error-free performance, we introduce and make public a more challenging dataset for multi-plane fitting evaluation.

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Section: Comparison With Multi-homography Fitting Techniquesmentioning

confidence: 96%

Multi-H: Efficient recovery of tangent planes in stereo images

Baráth¹,

Matas²,

Hajder³

2016

Procedings of the British Machine Vision Conference 2016

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“…Comparison of multi-model fitting algorithms. We compare Prog-X, Multi-X 3 , [3] RansaCov [19], RPA [18], T-Linkage 4 [17], and PEARL [5] on line fitting in this sections. All methods were applied five times to all scenes with fixed parameters.…”

Section: Synthesized Testsmentioning

confidence: 99%

“…Most recent approaches for multi-model fitting [31,15,21,17,18,29,19,3,1] follow a two-step procedure, first, generating a number of instances using RANSAC-like hypothesis generation. Second, a subset of the generated hypotheses is selected interpreting the input data points the most.…”

Section: Introductionmentioning

confidence: 99%

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Progressive-X: Efficient, Anytime, Multi-Model Fitting Algorithm

Baráth

Matas

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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The Progressive-X algorithm, Prog-X in short, is proposed for geometric multi-model fitting. The method interleaves sampling and consolidation of the current data interpretation via repetitive hypothesis proposal, fast rejection, and integration of the new hypothesis into the kept instance set by labeling energy minimization. Due to exploring the data progressively, the method has several beneficial properties compared with the state-of-the-art. First, a clear criterion, adopted from RANSAC, controls the termination and stops the algorithm when the probability of finding a new model with a reasonable number of inliers falls below a threshold. Second, Prog-X is an any-time algorithm. Thus, whenever is interrupted, e.g. due to a time limit, the returned instances cover real and, likely, the most dominant ones. The method is superior to the state-of-theart in terms of accuracy in both synthetic experiments and on publicly available real-world datasets for homography, two-view motion, and motion segmentation.

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“…A preliminary version of this paper appeared as [1]. In this paper we expanded the introductory material with additional insights, and added some new experiments on 3D point clouds.…”

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confidence: 99%

Multiple structure recovery with maximum coverage

Magri

Fusiello

2017

Machine Vision and Applications

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We present a general framework for geometric model fitting based on a set coverage formulation, that caters for intersecting structures and outliers in a simple and principled manner. The multi-model fitting problem is formulated in terms of the optimization of a consensus-based global cost function, which allows to sidestep the pitfalls of preference approaches based on clustering and to avoid the difficult trade-off between data fidelity and complexity of other optimization formulations. Two especially appealing characteristics of this method are the ease with which it can be implemented and its modularity with respect to the solver and to the sampling strategy. Few intelligible parameters need to be set and tuned, namely the inlier threshold and the number of desired models. The summary of the experiments is that our method compares favourably with its competitors overall, and it is always either the best performer or almost on par with the best performer in specific scenarios.

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Multiple Models Fitting as a Set Coverage Problem

Cited by 58 publications

References 33 publications

Multi-H: Efficient recovery of tangent planes in stereo images

Multi-H: Efficient recovery of tangent planes in stereo images

Progressive-X: Efficient, Anytime, Multi-Model Fitting Algorithm

Multiple structure recovery with maximum coverage

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