Robust Focal Length Estimation by Voting in Multi-view Scene Reconstruction

Bujnak, Martin; Kúkelová, Zuzana; Pajdla, Tomáš

doi:10.1007/978-3-642-12307-8_2

Cited by 11 publications

(11 citation statements)

References 26 publications

(52 reference statements)

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“…The proposed algorithm can also be applied in reconstruction or multi-view pipelines, e.g. that of Bujnak et al [8], if at least two images of the same camera with fixed focal length are available. Proof.…”

Section: Resultsmentioning

confidence: 99%

A Minimal Solution for Two-View Focal-Length Estimation Using Two Affine Correspondences

Baráth

Toth

Hajder

2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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A minimal solution using two affine correspondences is presented to estimate the common focal length and the fundamental matrix between two semi-calibrated camerasknown intrinsic parameters except a common focal length. To the best of our knowledge, this problem is unsolved. The proposed approach extends point correspondence-based techniques with linear constraints derived from local affine transformations. The obtained multivariate polynomial system is efficiently solved by the hidden-variable technique. Observing the geometry of local affinities, we introduce novel conditions eliminating invalid roots. To select the best one out of the remaining candidates, a root selection technique is proposed outperforming the recent ones especially in case of high-level noise. The proposed 2-point algorithm is validated on both synthetic data and 104 publicly available real image pairs. A Matlab implementation of the proposed solution is included in the paper.

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Section: Resultsmentioning

confidence: 99%

A Minimal Solution for Two-View Focal-Length Estimation Using Two Affine Correspondences

Baráth

Toth

Hajder

2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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“…Another alternative to speed up the process would be to use strategies such as Kernel voting [3] to directly pick a solution from the set of minimal equations, based on how well they satisfy the distance constraints. This would remove the need to repetitively calculate and test reprojection error.…”

Section: Discussionmentioning

confidence: 99%

Exhaustive Linearization for Robust Camera Pose and Focal Length Estimation

Peñate-Sánchez

Andrade‐Cetto

Moreno-Noguer

2013

IEEE Trans. Pattern Anal. Mach. Intell.

123

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Abstract-We propose a novel approach for the estimation of the pose and focal length of a camera from a set of 3D-to-2D point correspondences. Our method compares favorably to competing approaches in that it is both more accurate than existing closed form solutions, as well as faster and also more accurate than iterative ones. Our approach is inspired on the EPnP algorithm, a recent O(n) solution for the calibrated case. Yet, we show that considering the focal length as an additional unknown renders the linearization and relinearization techniques of the original approach no longer valid, especially with large amounts of noise. We present new methodologies to circumvent this limitation termed exhaustive linearization and exhaustive relinearization which perform a systematic exploration of the solution space in closed form. The method is evaluated on both real and synthetic data, and our results show that besides producing precise focal length estimation, the retrieved camera pose is almost as accurate as the one computed using the EPnP, which assumes a calibrated camera.

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“…In order to recover f , we applied [6] 6 to a number of 6-sized subsets (20 times the point number of the current motion) of the ground truth correspondences regarding to each motion. Finally, weighted histogram voting [32] was used to select the best candidate out of the obtained focal lengths.…”

Section: Application: Multi-motion Fittingmentioning

confidence: 99%

Efficient Recovery of Essential Matrix From Two Affine Correspondences

Baráth

Hajder

2018

IEEE Trans. on Image Process.

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We propose a method to estimate the essential matrix using two affine correspondences for a pair of calibrated perspective cameras. Two novel, linear constraints are derived between the essential matrix and a local affine transformation. The proposed method is also applicable to the over-determined case. We extend the normalization technique of Hartley to local affinities and show how the intrinsic camera matrices modify them. Even though perspective cameras are assumed, the constraints can straightforwardly be generalized to arbitrary camera models since they describe the relationship between local affinities and epipolar lines (or curves). Benefiting from the low number of exploited points, it can be used in robust estimators, e.g. RANSAC, as an engine, thus leading to significantly less iterations than the traditional point-based methods. The algorithm is validated both on synthetic and publicly available data sets and compared with the state-of-the-art. Its applicability is demonstrated on two-view multi-motion fitting, i.e., finding multiple fundamental matrices simultaneously, and outlier rejection.

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Robust Focal Length Estimation by Voting in Multi-view Scene Reconstruction

Cited by 11 publications

References 26 publications

A Minimal Solution for Two-View Focal-Length Estimation Using Two Affine Correspondences

A Minimal Solution for Two-View Focal-Length Estimation Using Two Affine Correspondences

Exhaustive Linearization for Robust Camera Pose and Focal Length Estimation

Efficient Recovery of Essential Matrix From Two Affine Correspondences

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