Pose estimation with radial distortion and unknown focal length

Josephson, K.; Byrod, M.

doi:10.1109/cvprw.2009.5206756

Cited by 34 publications

(71 citation statements)

References 17 publications

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“…This behavior has already been reported by Sattler et al [30]. They show that using the focal length of an image, obtained from its EXIF tag, for 3-point pose estimation [13,16] or a more restrictive camera model, which estimates only its focal length and a radial distortion parameter [21], help to obtain more accurate estimates. We could also estimate the covariance of the position parameters of the query camera and reject a camera for which the positional uncertainty is too high.…”

Section: Localization Accuracysupporting

confidence: 78%

Towards Fast Image-Based Localization on a City-Scale

Sattler

Leibe

Kobbelt

2012

Lecture Notes in Computer Science

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Abstract. Recent developments in Structure-from-Motion approaches allow the reconstructions of large parts of urban scenes. The available models can in turn be used for accurate image-based localization via pose estimation from 2D-to-3D correspondences. In this paper, we analyze a recently proposed localization method that achieves state-of-the-art localization performance using a visual vocabulary quantization for efficient 2D-to-3D correspondence search. We show that using only a subset of the original models allows the method to achieve a similar localization performance. While this gain can come at additional computational cost depending on the dataset, the reduced model requires significantly less memory, allowing the method to handle even larger datasets. We study how the size of the subset, as well as the quantization, affect both the search for matches and the time needed by RANSAC for pose estimation.

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Section: Localization Accuracysupporting

confidence: 78%

Towards Fast Image-Based Localization on a City-Scale

Sattler

Leibe

Kobbelt

2012

Lecture Notes in Computer Science

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“…Josephson and Byröd gave a minimal method for estimating camera pose, together with the focal length and one radial distortion coefficient (division model), from four matches [259]. A special case is that of planar pose, where the unknown object pose consists of a translation in one plane (usually, a horizontal ground plane) and a rotation about the normal of that plane.…”

Section: Pose Estimationmentioning

confidence: 99%

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Sturm

2010

FNT in Computer Graphics and Vision

157

122

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A printed and bound version is available at a special discount price of US35 from Now Publishers. This can be obtained by entering the promotional code CGV006023 on https://www.nowpublishers.com/bookorder.aspx?doi=0600000023&product=CGVInternational audienceThis survey is mainly motivated by the increased availability and use of panoramic image acquisition devices, in computer vision and various of its applications. Different technologies and different computational models thereof exist and algorithms and theoretical studies for geometric computer vision ("structure-from-motion") are often re-developed without highlighting common underlying principles. One of the goals of this survey is to give an overview of image acquisition methods used in computer vision and especially, of the vast number of camera models that have been proposed and investigated over the years, where we try to point out similarities between different models. Results on epipolar and multi-view geometry for different camera models are reviewed as well as various calibration and self-calibration approaches, with an emphasis on non-perspective cameras. We finally describe what we consider are fundamental building blocks for geometric computer vision or structure-from-motion: epipolar geometry, pose and motion estimation, 3D scene modeling, and bundle adjustment. The main goal here is to highlight the main principles of these, which are independent of specific camera models

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“…so that all image coordinates are mapped between minus one and one (Josephson and Byrod, 2009). This way, the analysis is independent of image size.…”

Section: Comparing the Self-calibration And The Benchmark Calibrationmentioning

confidence: 99%

“…lens distortion if the effects are negligible (Josephson and Byrod, 2009), which we assume is the case for this high-quality lens we used.…”

Section: Self-calibration Versus Benchmark Calibrationmentioning

confidence: 99%

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Quality Assessment of Self-Calibration with Distortion Estimation for Grid Point Images

Franjcic

Bondeson²

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Recently, a camera self-calibration algorithm was reported which solves for pose, focal length and radial distortion using a minimal set of four 2D-to-3D point correspondences. In this paper, we present an empirical analysis of the algorithm's accuracy using highfidelity point correspondences. In particular, we use images of circular markers arranged in a regular planar grid, obtain the centroids of the marker images, and pass those as input point correspondences to the algorithm. We compare the resulting reprojection errors against those obtained from a benchmark calibration based on the same data. Our experiments show that for low-noise point images the self-calibration technique performs at least as good as the benchmark with a simplified distortion model.

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Pose estimation with radial distortion and unknown focal length

Abstract: Abstract

Cited by 34 publications

References 17 publications

Towards Fast Image-Based Localization on a City-Scale

Towards Fast Image-Based Localization on a City-Scale

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Quality Assessment of Self-Calibration with Distortion Estimation for Grid Point Images

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