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On Depth Error from Spherical Camera Calibration within Omnidirectional Stereo Vision [dataset] Open Access

As a depth sensing approach, whilst stereo vision provides a good compromise between accuracy and cost, a key limitation is the limited field of view of the conventional cameras that are used within most stereo configurations. By contrast, the use of spherical cameras within a stereo configuration offers omnidirectional stereo sensing. However, despite the presence of significant image distortion in spherical camera images, only very limited attempts have been made to study and quantify omnidirectional stereo depth accuracy. In this paper we construct such an omnidirectional stereo system that is capable of real-time 360 degree disparity map reconstruction as the basis for such a study. We first investigate the accuracy of using a standard spherical camera model for calibration combined with a longitude-latitude projection for omnidirectional stereo, and show that the depth error increases significantly as the angle from the camera optical axis approaches the limits of the camera field of view. In contrast, we then consider an alternative calibration approach via the use of perspective undistortion with a conventional pinhole camera model allowing omnidirectional cameras to be mapped to a conventional rectilinear stereo formulation. We find that conversely this proposed approach exhibits improved depth accuracy at large angles from the camera optical axis when compared to omnidirectional stereo depth based on a spherical camera model calibration. [ On Depth Error from Spherical Camera Calibration within Omnidirectional Stereo Vision (M. Groom, T.P. Breckon), In Proc. Int. Conf. on Pattern Recognition, IEEE, 2022. ] Supporting dataset - all stereo camera calibration and depth evaluation images used in this study.

Descriptions

Resource type
Dataset
Contributors
Creator: Groom, Michael 1
Editor: Breckon, Toby 1
1 Durham University, UK
Funder
Durham University, UK
Research methods
Numerical methods in computational and multiple view geometry.
Other description
Predominantly image files in PNG format.
Keyword
Stereo vision
Spherical camera
Angular disparity
Stereo calibration
Computer vision
3D vision
Subject
Engineering
Computer science
Stereoscopic views
Cameras
Location
Durham, England, United Kingdom
Language
English
Cited in
On Depth Error from Spherical Camera Calibration within Omnidirectional Stereo Vision (M. Groom, T.P. Breckon), In Proc. Int. Conf. on Pattern Recognition, IEEE, 2022.
Identifier
ark:/32150/r13197xm075
doi:10.15128/r13197xm075
Rights
Creative Commons Attribution 4.0 International (CC BY)

Publisher
Durham University
Date Created
March 2021

File Details

Depositor
T. Breckon
Date Uploaded
Date Modified
12 June 2022, 23:06:12
Audit Status
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Characterization
File format: zip (ZIP Format)
Mime type: application/zip
File size: 352150738
Last modified: 2022:06:12 23:36:57+01:00
Filename: groom-icpr-2022_v2.zip
Original checksum: d0418b7a9285843d74214bc13282aeab
Activity of users you follow
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User N. Syrotiuk has updated On Depth Error from Spherical Camera Calibration within Omnidirectional Stereo Vision [dataset] 2 months ago
User N. Syrotiuk has updated On Depth Error from Spherical Camera Calibration within Omnidirectional Stereo Vision [dataset] 2 months ago
User N. Syrotiuk has updated On Depth Error from Spherical Camera Calibration within Omnidirectional Stereo Vision [ dataset ] 2 months ago