Affine Correspondences Between Central Cameras for Rapid Relative Pose Estimation

Eichhardt, Ivan; Chetverikov, Dmitry

doi:10.1007/978-3-030-01231-1_30

Cited by 22 publications

(32 citation statements)

References 26 publications

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“…One AC provides a 2 × 2 affine transformation matrix between the local patches around the corresponding points in two images. Two-view geometry of AC has been investigated [15], [16], [18], [19], and several methods of relative pose from ACs has been proposed [15], [16], [17], [18], [19], [20]. In addition, there are relative pose estimation methods [27], [28], [29] exploit orientation-and scalecovariant features obtained by e.g.…”

Section: Related Workmentioning

confidence: 99%

“…Single camera and ACs: One AC usually provides 3 independent constraints on relative pose [15]. As a result, two ACs are enough to determine the relative pose for a single camera [16], [18], [19] or a single camera with unknown focal length [17]. In [20], guidelines are proposed for effective usage of ACs in the course of a full model estimation pipeline.…”

Section: Related Workmentioning

confidence: 99%

“…Thus much effort has been made to reduce the required number of feature correspondences in minimal solvers while keeping their efficiency. In recent years, there are a series of work exploiting affine correspondences (ACs) to estimate relative pose [15], [16], [17], [18], [19], [20]. Since one AC provides more independent constraints than one PC, fewer ACs than PCs are needed to solve pose estimation problems.…”

Section: Introductionmentioning

confidence: 99%

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On Relative Pose Recovery for Multi-Camera Systems

Zhao¹,

Guan²

2021

Preprint

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The point correspondence (PC) and affine correspondence (AC) are widely used for relative pose estimation. An AC consists of a PC across two views and an affine transformation between the small patches around this PC. Previous work demonstrates that one AC generally provides three independent constraints for relative pose estimation. For multi-camera systems, there is still not any AC-based minimal solver for general relative pose estimation. To deal with this problem, we propose a complete solution to relative pose estimation from two ACs for multi-camera systems, consisting of a series of minimal solvers. The solver generation in our solution is based on Cayley or quaternion parameterization for rotation and hidden variable technique to eliminate translation. This solver generation method is also naturally applied to relative pose estimation from PCs, resulting in a new six-point method for multi-camera systems. A few extensions are made, including relative pose estimation with known rotation angle and/or with unknown focal lengths. Extensive experiments demonstrate that the proposed AC-based solvers and PC-based solvers are effective and efficient on synthetic and real-world datasets.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On Relative Pose Recovery for Multi-Camera Systems

Zhao¹,

Guan²

2021

Preprint

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“…This allows the estimation of a fundamental matrix from 3 ACs [7]. The estimation of a homography and an essential matrix can be accomplished from 2 ACs [32,12,3]. There is an independent work which also uses a single AC to estimate relative planar motion [18].…”

Section: Related Workmentioning

confidence: 99%

Minimal Solutions for Relative Pose With a Single Affine Correspondence

Guan

Zhao

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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In this paper we present four cases of minimal solutions for two-view relative pose estimation by exploiting the affine transformation between feature points and we demonstrate efficient solvers for these cases. It is shown, that under the planar motion assumption or with knowledge of a vertical direction, a single affine correspondence is sufficient to recover the relative camera pose. The four cases considered are two-view planar relative motion for calibrated cameras as a closed-form and a least-squares solution, a closedform solution for unknown focal length and the case of a known vertical direction. These algorithms can be used efficiently for outlier detection within a RANSAC loop and for initial motion estimation. All the methods are evaluated on both synthetic data and real-world datasets from the KITTI benchmark. The experimental results demonstrate that our methods outperform comparable state-of-the-art methods in accuracy with the benefit of a reduced number of needed RANSAC iterations.

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“…Baráth et al [3,6] showed that the relationship of ACs and epipolar geometry is linear and geometrically interpretable. Eichhardt et al [14] proposed a method that uses two ACs for relative pose estimation based on general central-projective views. Recently, Hajder et al [19] and Guan et al [18] proposed minimal solutions for relative pose from a single AC when the camera is mounted on a moving vehicle.…”

Section: Introductionmentioning

confidence: 99%

Relative Pose from Deep Learned Depth and a Single Affine Correspondence

Eichhardt

Baráth

2020

Lecture Notes in Computer Science

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We propose a new approach for combining deep-learned nonmetric monocular depth with affine correspondences (ACs) to estimate the relative pose of two calibrated cameras from a single correspondence. Considering the depth information and affine features, two new constraints on the camera pose are derived. The proposed solver is usable within 1-point RANSAC approaches. Thus, the processing time of the robust estimation is linear in the number of correspondences and, therefore, orders of magnitude faster than by using traditional approaches. The proposed 1AC+D 4 solver is tested both on synthetic data and on 110 395 publicly available real image pairs where we used an off-the-shelf monocular depth network to provide up-to-scale depth per pixel. The proposed 1AC+D leads to similar accuracy as traditional approaches while being significantly faster. When solving large-scale problems, e.g. pose-graph initialization for Structure-from-Motion (SfM) pipelines, the overhead of obtaining ACs and monocular depth is negligible compared to the speed-up gained in the pairwise geometric verification, i.e., relative pose estimation. This is demonstrated on scenes from the 1DSfM dataset using a state-of-the-art global SfM algorithm.

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Affine Correspondences Between Central Cameras for Rapid Relative Pose Estimation

Cited by 22 publications

References 26 publications

On Relative Pose Recovery for Multi-Camera Systems

On Relative Pose Recovery for Multi-Camera Systems

Minimal Solutions for Relative Pose With a Single Affine Correspondence

Relative Pose from Deep Learned Depth and a Single Affine Correspondence

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