A Minimum Error Vanishing Point Detection Approach for Uncalibrated Monocular Images of Man-Made Environments

Xu, Yifan; Oh, Sung Jong; Hoogs, Anthony

doi:10.1109/cvpr.2013.181

Cited by 63 publications

(67 citation statements)

References 16 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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“…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%

“…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%

“…The VP computation or refinement with given associated lines is commonly done by Hough voting and nonmaximum suppression [21,24,20,32,23], by solving a quadratic program [2], implicitly in an ExpectationMaximization (EM) setting [1,27,29,35], or by linear least-squares, as in [15].…”

Section: Related Workmentioning

confidence: 99%

“…Accumulator space: The intersections of imaged lines are computed in the (unbounded) image space [26,27,29,9,2,35] or on a (bounded) Gaussian unit sphere, as introduced in [3] and used in [21,24,20,1,15,11,12,4,22,28].…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

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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A-Contrario Horizon-First Vanishing Point Detection Using Second-Order Grouping Laws

Simon

Fond

Berger

2018

Computer Vision – ECCV 2018

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We show that, in images of man-made environments, the horizon line can usually be hypothesized based on a-contrario detections of second-order grouping events. This allows constraining the extraction of the horizontal vanishing points on that line, thus reducing false detections. Experiments made on three datasets show that our method, not only achieves state-of-the-art performance w.r.t. horizon line detection on two datasets, but also yields much less spurious vanishing points than the previous top-ranked methods.

show abstract

A Minimum Error Vanishing Point Detection Approach for Uncalibrated Monocular Images of Man-Made Environments

Cited by 63 publications

References 16 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

Discovery of Sets of Mutually Orthogonal Vanishing Points in Videos

A-Contrario Horizon-First Vanishing Point Detection Using Second-Order Grouping Laws

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