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An Evaluation of Two Methodologies for Lens Distortion Removal when EXIF Data is Unavailable
ISSN: 0148-7191, e-ISSN: 2688-3627
Published March 28, 2017 by SAE International in United States
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Photogrammetry and the accuracy of a photogrammetric solution is reliant on the quality of photographs and the accuracy of pixel location within the photographs. A photograph with lens distortion can create inaccuracies within a photogrammetric solution. Due to the curved nature of a camera’s lens(s), the light coming through the lens and onto the image sensor can have varying degrees of distortion. There are commercially available software titles that rely on a library of known cameras, lenses, and configurations for removing lens distortion. However, to use these software titles the camera manufacturer, model, lens and focal length must be known. This paper presents two methodologies for removing lens distortion when camera and lens specific information is not available. The first methodology uses linear objects within the photograph to determine the amount of lens distortion present. This method will be referred to as the straight-line method. The second methodology utilizes photogrammetry principles and 3D point cloud data to solve for and remove lens distortion. This method will be referred to as the point cloud method. Using cameras with known distortion parameters, both methodologies are presented and individually evaluated against publically available, library-based, distortion removal solutions. Based on the results of lens distortion removal from cameras with known lens distortion, the straight-line method was found to improve pixel location within a photograph by an average of 82 percent and by as much as 99 percent. The point cloud method was found to improve pixel location by an average of 40 percent and by as much as 66 percent.
CitationTerpstra, T., Miller, S., and Hashemian, A., "An Evaluation of Two Methodologies for Lens Distortion Removal when EXIF Data is Unavailable," SAE Technical Paper 2017-01-1422, 2017, https://doi.org/10.4271/2017-01-1422.
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