An Exact Formula for Calculating Inverse Radial Lens Distortions

Drap, Pierre; Lefèvre, Julien

doi:10.3390/s16060807

Cited by 62 publications

(40 citation statements)

References 26 publications

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“…If a set of parameters is assigned to each image, the bundle is called 'photo-variant' selfcalibration (Moniwa, 1981). All available processing software applications include various variants of additional parameters but the values of these parameters are generally not directly comparable (Drap and Lefevre, 2016). Indeed, they may be normalized to the focal length value and in some cases are provided as correction values, in others as proper distortion parameters.…”

Section: Adopted Terminologymentioning

confidence: 99%

A Critical Review of Automated Photogrammetric Processing of Large Datasets

Remondino

Nocerino

Toschi

et al. 2017

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

128

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ABSTRACT:The paper reports some comparisons between commercial software able to automatically process image datasets for 3D reconstruction purposes. The main aspects investigated in the work are the capability to correctly orient large sets of image of complex environments, the metric quality of the results, replicability and redundancy. Different datasets are employed, each one featuring a diverse number of images, GSDs at cm and mm resolutions, and ground truth information to perform statistical analyses of the 3D results. A summary of (photogrammetric) terms is also provided, in order to provide rigorous terms of reference for comparisons and critical analyses.

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Section: Adopted Terminologymentioning

confidence: 99%

A Critical Review of Automated Photogrammetric Processing of Large Datasets

Remondino

Nocerino

Toschi

et al. 2017

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

128

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“…For methodology (3), the calibration parameters obtained using FOTOGIFLE were imported into PhotoScan in Australis format (Fraser and Edmundson, ) and then transformed by PhotoScan into its own format. This process was necessary as the calibration parameters are defined differently in each software (Drap and Lefèvre, ).…”

Section: Methodsmentioning

confidence: 99%

Smartphone‐based close‐range photogrammetric assessment of spherical objects

Barbero-García

Cabrelles

Lerma

et al. 2018

The Photogrammetric Record

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Smartphones have widened the possibilities for low-cost close-range image acquisition for three-dimensional (3D) modelling. They allow the rapid acquisition of large amounts of data for a wide range of applications. However, the accuracy of the models and the automation possibilities depend on the image acquisition conditions and application requirements. In this study, the accuracy and reliability of the derived photogrammetric 3D models are evaluated on a spherical set-up for close-range applications (c.30 cm). Different numbers of images, network configurations, targets, devices and camera calibration methodologies are tested and evaluated. Results show that for this close-range application high accuracy (0Á2 mm) and reliability can be achieved. The number of images did not significantly affect the accuracy but was vital for tie-point detection and image orientation. The use of artificial targets was found to be the key factor in increasing the final accuracy. In contrast, the image calibration strategy and the characteristics of the imaging device did not have a great impact on the results.Smartphone video (more specifically slow-motion video) is a useful tool for acquiring large numbers of images, suitable even for fast moving objects. These images can be used for the creation of 3D models of moving objects (Barbero-Garc ıa et al., 2017). With an image acquisition speed of 240 frames per second (fps) of many smartphones (still far below the ultra-high-speed cameras that reach up to 2000 fps), the computational cost is the main limitation given the number of images to be used for 3D modelling.Despite their advantages, smartphone cameras present high internal instability that hampers their correct calibration. This problem is common to all non-metric digital cameras (Fraser, 2013), but is especially exacerbated when working with smartphones. The radiometric accuracy of smartphone cameras is lower than that of single-lens reflex (SLR) cameras but, despite their limitations, studies have concluded that these cameras can be used for photogrammetric tasks with a required accuracy of 1:10 000 (Akca and Gruen, 2009).The development of useful tools, which could allow non-expert users to obtain accurate 3D models for different purposes, requires a high degree of automation (Remondino et al., 2014). However, most of the available automatic low-cost solutions provide low repeatability and reliability (Remondino et al., 2012). The development of fully automatic and reliable solutions for specific applications requires an extensive knowledge of the factors affecting the quality of the 3D models created using smartphones or other similar imaging devices, such as tablets. The most important factors include the determination of the ideal geometric network, the selection of the best video frames and their optimal number, as well as the accuracy requirements for camera calibration. These parameters can vary greatly depending on the characteristics and limitations of the image acquisition process for a specific application (suc...

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“…We use a simple distortion model (such as the one in [18]) with one radial distortion parameter denoted by k in (19). Figure 3 shows the pincushion and barrel distortions, which depend on the parameter k. The rectangle shows the receptive field of the camera, which corresponds to the image in pixels, and the red crosses stand for the T targets to be sampled during calibration.…”

Section: Simulationsmentioning

confidence: 99%

Audio-Visual Calibration with Polynomial Regression for 2-D Projection Using SVD-PHAT

Grondin

Tang

Glass

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper proposes a straightforward 2-D method to spatially calibrate the visual field of a camera with the auditory field of an array microphone by generating and overlaying an acoustic image over an optical image. Using a low-cost microphone array and an off-the-shelf camera, we show that polynomial regression can deal efficiently with non-linear camera distortion, and that a recently proposed sound source localization method for real-time processing, SVD-PHAT, can be adapted for this task.

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An Exact Formula for Calculating Inverse Radial Lens Distortions

Cited by 62 publications

References 26 publications

A Critical Review of Automated Photogrammetric Processing of Large Datasets

A Critical Review of Automated Photogrammetric Processing of Large Datasets

Smartphone‐based close‐range photogrammetric assessment of spherical objects

Audio-Visual Calibration with Polynomial Regression for 2-D Projection Using SVD-PHAT

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