Finding Vanishing Points via Point Alignments in Image Primal and Dual Domains

Lezama, José Luis; Gioi, Rafael Grompone von; Randall, Grégory; Morel, Jean‐Michel

doi:10.1109/cvpr.2014.72

Cited by 86 publications

(130 citation statements)

References 17 publications

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“…However, such a calibration has a poor accuracy. The reason is that existing methods for VP detection are not assessed on motion estimation; they are generally tuned regarding line clustering capacities as well as zenith and horizon estimation [1,2], with arbitrary ground truths. Moreover, VPs are theoretical constructs; they are not real.…”

Section: Introductionmentioning

confidence: 99%

Robust and Accurate Line- and/or Point-Based Pose Estimation without Manhattan Assumptions

Salaün

Marlet

Monasse

2016

Computer Vision – ECCV 2016

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Abstract. Usual Structure from Motion techniques based on feature points have a hard time on scenes with little texture or presenting a single plane, as in indoor environments. Line segments are more robust features in this case. We propose a novel geometrical criterion for two-view pose estimation using lines, that does not assume a Manhattan world. We also define a parameterless (a contrario) RANSAC-like method to discard calibration outliers and provide more robust pose estimations, possibly using points as well when available. Last, we provide quantitative experimental data that illustrate failure cases of other methods and that show how our approach outperforms them, both in robustness and accuracy.

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Section: Introductionmentioning

confidence: 99%

Robust and Accurate Line- and/or Point-Based Pose Estimation without Manhattan Assumptions

Salaün

Marlet

Monasse

2016

Computer Vision – ECCV 2016

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“…length in the a contrario test. Using more sophisticated and specific schemes to obtain the vanishing point candidates might also lead to further improvements [31].…”

Section: Resultsmentioning

confidence: 99%

A Biclustering Framework for Consensus Problems

Tepper¹,

Sapiro²

2014

SIAM J. Imaging Sci.

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Abstract. We consider grouping as a general characterization for problems such as clustering, community detection in networks, and multiple parametric model estimation. We are interested in merging solutions from different grouping algorithms, distilling all their good qualities into a consensus solution. In this paper, we propose a bi-clustering framework and perspective for reaching consensus in such grouping problems. In particular, this is the first time that the task of finding/fitting multiple parametric models to a dataset is formally posed as a consensus problem. We highlight the equivalence of these tasks and establish the connection with the computational Gestalt program, that seeks to provide a psychologically-inspired detection theory for visual events. We also present a simple but powerful bi-clustering algorithm, specially tuned to the nature of the problem we address, though general enough to handle many different instances inscribed within our characterization. The presentation is accompanied with diverse and extensive experimental results in clustering, community detection, and multiple parametric model estimation in image processing applications.

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“…Line-VP consistency and VP refinement: The consistency between an estimated VP and the image lines is usually measured using line endpoint distances in the image, used by us and [29,12,4,2,17], the angular differences in the image [26,8], with explicit probabilistic modeling of the line end point errors [35], or with angles between normals of interpretation planes in the Gaussian sphere [21,24,20]. We sample MOVP candidates on the Gaussian sphere because testing for orthogonality directly translates to vector cross products, but revert to image-space fitting errors for refinement to avoid distorted errors and to attenuate dependance on (potentially) noisy internal camera calibration.…”

Section: Related Workmentioning

confidence: 99%

“…Efficient search [26,8], direct clustering [29,17], multi-line RANSAC [4,34], EM procedures [1,15,27,12,35], or MCMC inference [28] are among the methods employed directly on the accumulator space. If a discretization is enforced on the accumalator space, the solutions are found by voting schemes [21,24,20,23] or inference over graphical models [30,2].…”

Section: Related Workmentioning

confidence: 99%

Discovery of Sets of Mutually Orthogonal Vanishing Points in Videos

Kroeger¹,

Dai²,

Timofte³

et al. 2015

2015 IEEE Winter Applications and Computer Vision Workshops

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While vanishing point (VP) estimation has received extensive attention, most approaches focus on static images or perform detection and tracking separately. In this paper, we focus on man-made environments and propose a novel method for detecting and tracking groups of mutually orthogonal vanishing points (MOVP), also known as Manhattan frames, jointly from monocular videos. The method is unique in that it is designed to enforce orthogonality in groups of VPs, temporal consistency of each individual MOVP, and orientation consistency of all putative MOVP. To this end, the method consists of three steps: 1) proposal of MOVP candidates by directly incorporating mutual orthogonality; 2) extracting consistent tracks of MOVPs by minimizing the flow cost over a network where nodes are putative MOVPs and edges are putative links across time; and 3) refinement of all MOVPs by enforcing consistency between lines, their identified vanishing directions and consistency of global camera orientation. The method is evaluated on six newly collected and annotated videos of urban scenes. Extensive experiments show that the method outperforms greedy MOVP tracking method considerably. In addition, we also test the method for camera orientation estimation and show that it obtains very promising results on a challenging street-view dataset.

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Finding Vanishing Points via Point Alignments in Image Primal and Dual Domains

Cited by 86 publications

References 17 publications

Robust and Accurate Line- and/or Point-Based Pose Estimation without Manhattan Assumptions

Robust and Accurate Line- and/or Point-Based Pose Estimation without Manhattan Assumptions

A Biclustering Framework for Consensus Problems

Discovery of Sets of Mutually Orthogonal Vanishing Points in Videos

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