article

Single View Metrology

Authors:

A. ZissermanAuthors Info & Claims

International Journal of Computer Vision, Volume 40, Issue 2

Pages 123 - 148

https://doi.org/10.1023/A:1026598000963

Published: 01 November 2000 Publication History

Abstract

We describe how 3D affine measurements may be computed from a single perspective view of a scene given only minimal geometric information determined from the image. This minimal information is typically the vanishing line of a reference plane, and a vanishing point for a direction not parallel to the plane. It is shown that affine scene structure may then be determined from the image, without knowledge of the camera's internal calibration (e.g. focal length), nor of the explicit relation between camera and world (pose).

In particular, we show how to (i) compute the distance between planes parallel to the reference plane (up to a common scale factor); (ii) compute area and length ratios on any plane parallel to the reference plane; (iii) determine the camera's location. Simple geometric derivations are given for these results. We also develop an algebraic representation which unifies the three types of measurement and, amongst other advantages, permits a first order error propagation analysis to be performed, associating an uncertainty with each measurement.

We demonstrate the technique for a variety of applications, including height measurements in forensic images and 3D graphical modelling from single images.

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Cited By

Kluger FBrachmann EYang MRosenhahn B(2024)Robust Shape Fitting for 3D Scene AbstractionIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.337901446:9(6306-6325)Online publication date: 1-Sep-2024
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Hold-Geoffroy YPiché-Meunier DSunkavalli KBazin JRameau FLalonde J(2023)A Perceptual Measure for Deep Single Image Camera and Lens CalibrationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2023.326964145:9(10603-10614)Online publication date: 1-Sep-2023
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Index Terms

Single View Metrology
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
      2. Image and video acquisition
        3D imaging
  2. Machine learning

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Copyright © Copyright © 2000 Kluwer Academic Publishers.

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Kluwer Academic Publishers

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Published: 01 November 2000

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