Algorithms for the Orthographic-n-Point Problem

Steger, Carsten

doi:10.1007/s10851-017-0756-y

Cited by 7 publications

(6 citation statements)

References 72 publications

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“…With known initial values for the interior orientation, the image points p jl can be transformed into metric coordinates in the camera coordinate system using ( 31)-( 33). This allows us to use the OnP algorithm described by Steger (2018) to obtain estimates for the exterior orientation of the calibration object.…”

Section: Calibrationmentioning

confidence: 99%

A Camera Model for Line-Scan Cameras with Telecentric Lenses

Steger

Ulrich

2020

Int J Comput Vis

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We propose a camera model for line-scan cameras with telecentric lenses. The camera model assumes a linear relative motion with constant velocity between the camera and the object. It allows to model lens distortions, while supporting arbitrary positions of the line sensor with respect to the optical axis. We comprehensively examine the degeneracies of the camera model and propose methods to handle them. Furthermore, we examine the relation of the proposed camera model to affine cameras. In addition, we propose an algorithm to calibrate telecentric line-scan cameras using a planar calibration object. We perform an extensive evaluation of the proposed camera model that establishes the validity and accuracy of the proposed model. We also show that even for lenses with very small lens distortions, the distortions are statistically highly significant. Therefore, they cannot be omitted in real-world applications.

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Section: Calibrationmentioning

confidence: 99%

A Camera Model for Line-Scan Cameras with Telecentric Lenses

Steger

Ulrich

2020

Int J Comput Vis

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“…The initial value of v k,z usually can also be set to 0. With known initial values for the interior orientation, the control point coordinates p j and their corresponding image point coordinates p jkl can be used as input for the OnP algorithm described in [41] to obtain estimates for the exterior orientations e l of the calibration object. These, in turn, can be used to compute initial estimates for the relative orientations r k .…”

Section: Calibrationmentioning

confidence: 99%

A Multi-view Camera Model for Line-Scan Cameras with Telecentric Lenses

Steger

Ulrich

2021

J Math Imaging Vis

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We propose a novel multi-view camera model for line-scan cameras with telecentric lenses. The camera model supports an arbitrary number of cameras and assumes a linear relative motion with constant velocity between the cameras and the object. We distinguish two motion configurations. In the first configuration, all cameras move with independent motion vectors. In the second configuration, the cameras are mounted rigidly with respect to each other and therefore share a common motion vector. The camera model can model arbitrary lens distortions by supporting arbitrary positions of the line sensor with respect to the optical axis. We propose an algorithm to calibrate a multi-view telecentric line-scan camera setup. To facilitate a 3D reconstruction, we prove that an image pair acquired with two telecentric line-scan cameras can always be rectified to the epipolar standard configuration, in contrast to line-scan cameras with entocentric lenses, for which this is possible only under very restricted conditions. The rectification allows an arbitrary stereo algorithm to be used to calculate disparity images. We propose an efficient algorithm to compute 3D coordinates from these disparities. Experiments on real images show the validity of the proposed multi-view telecentric line-scan camera model.

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“…Figure 2: Two views statistical shape prior. The 3D structure S is drawn from a statistical shape distribution using neural shape priors and consequently projected to 2 views using the cameras R * k ∀k ∈ [1, 2] -calculated through OnP formulation [46]. The proposed approach minimizes the 2D projection error between the predicted 2D projections Wk and target (input) 2D projections W k .…”

Section: Variable Type Examplesmentioning

confidence: 99%

“…Due to the bilinear form of (1), s is ambiguous and becomes up-to-scale recoverable only when S is assumed to follow certain prior statistics. We handle scale by approximating with an orthogonal projection and solving an Orthogonal-N-Point (OnP) problem [46] to find the camera pose along with the scale, as discussed in Sec. 4.2.…”

Section: Variable Type Examplesmentioning

confidence: 99%

High Fidelity 3D Reconstructions with Limited Physical Views

Dabhi¹,

Wang²,

Saluja³

et al. 2021

Preprint

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Multi-view triangulation is the gold standard for 3D reconstruction from 2D correspondences given known calibration and sufficient views. However in practice, expensive multi-view setups -involving tens sometimes hundreds of cameras -are required in order to obtain the high fidelity 3D reconstructions necessary for many modern applications. In this paper we present a novel approach that leverages recent advances in 2D-3D lifting using neural shape priors while also enforcing multi-view equivariance. We show how our method can achieve comparable fidelity to expensive calibrated multi-view rigs using a limited (2-3) number of uncalibrated camera views.

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Algorithms for the Orthographic-n-Point Problem

Cited by 7 publications

References 72 publications

A Camera Model for Line-Scan Cameras with Telecentric Lenses

A Camera Model for Line-Scan Cameras with Telecentric Lenses

A Multi-view Camera Model for Line-Scan Cameras with Telecentric Lenses

High Fidelity 3D Reconstructions with Limited Physical Views

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