Influence of camera calibration conditions on the accuracy of 3D reconstruction

Poulin-Girard, Anne-Sophie; Thibault, Simon; Laurendeau, Denis

doi:10.1364/oe.24.002678

Cited by 66 publications

(20 citation statements)

References 8 publications

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“…where and are scaling factors; and are intrinsic parameters of the camera and the projector pinhole model; y are transformation matrices from the coordinate system of the model ( , , ) and the camera and projector systems, respectively. In this paper, we consider the camera and projector distortion as [8], ,…”

Section: Stereo Calibrationmentioning

confidence: 99%

Camera-Projector Calibration Methods with Compensation of Geometric Distortions in Fringe Projection Profilometry: A Comparative Study

Vargas¹,

Marrugo²,

Pineda³

et al. 2018

Opt. Pura Apl.

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The calibration methods most used in fringe projection profilometry are based on models of least squares adjustment and stereo vision techniques. However, the geometric distortions of the projector and camera lenses introduce imprecision in certain regions of the 3D reconstruction. In this paper, we perform a comparative study between the second order polynomial adjustment method and the stereo calibration method applying lens distortion compensation. The experimental results show that in the stereo calibration the incidence of the distortions in the 3D reconstruction is significant. In contrast, in the proposed polynomial calibration, reconstruction errors are associated with the calibrated volume, typically low within the calibration volume.

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Section: Stereo Calibrationmentioning

confidence: 99%

Camera-Projector Calibration Methods with Compensation of Geometric Distortions in Fringe Projection Profilometry: A Comparative Study

Vargas¹,

Marrugo²,

Pineda³

et al. 2018

Opt. Pura Apl.

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show abstract

“…Vision measurement on the basis of structured light is a very significant way of reconstructing 3D object surfaces due to non-contact test, simple operation, high efficiency and good accuracy 3 – 6 . Nguyen 7 presents a real-time measurement method of the object shape with the advantages of high speed and accuracy.…”

Section: Introductionmentioning

confidence: 99%

3D reconstruction of laser projective point with projection invariant generated from five points on 2D target

Yuan

et al. 2017

Sci Rep

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Vision measurement on the basis of structured light plays a significant role in the optical inspection research. The 2D target fixed with a line laser projector is designed to realize the transformations among the world coordinate system, the camera coordinate system and the image coordinate system. The laser projective point and five non-collinear points that are randomly selected from the target are adopted to construct a projection invariant. The closed form solutions of the 3D laser points are solved by the homogeneous linear equations generated from the projection invariants. The optimization function is created by the parameterized re-projection errors of the laser points and the target points in the image coordinate system. Furthermore, the nonlinear optimization solutions of the world coordinates of the projection points, the camera parameters and the lens distortion coefficients are contributed by minimizing the optimization function. The accuracy of the 3D reconstruction is evaluated by comparing the displacements of the reconstructed laser points with the actual displacements. The effects of the image quantity, the lens distortion and the noises are investigated in the experiments, which demonstrate that the reconstruction approach is effective to contribute the accurate test in the measurement system.

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“…However, this method uses two or more cameras for the depth estimation. In order to estimate the 3D information using single camera, the camera calibration methods [8][9][10][11] are proposed. Arfaoui and Thibault used a diffractive virtual grid to estimate camera parameters for a fish-eye lens camera [12].…”

Section: Introductionmentioning

confidence: 99%

Object Detection and Tracking-Based Camera Calibration for Normalized Human Height Estimation

et al. 2016

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This paper presents a normalized human height estimation algorithm using an uncalibrated camera. To estimate the normalized human height, the proposed algorithm detects a moving object and performs tracking-based automatic camera calibration. The proposed method consists of three steps: (i) moving human detection and tracking, (ii) automatic camera calibration, and (iii) human height estimation and error correction. The proposed method automatically calibrates camera by detecting moving humans and estimates the human height using error correction. The proposed method can be applied to object-based video surveillance systems and digital forensic.

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Influence of camera calibration conditions on the accuracy of 3D reconstruction

Cited by 66 publications

References 8 publications

Camera-Projector Calibration Methods with Compensation of Geometric Distortions in Fringe Projection Profilometry: A Comparative Study

Camera-Projector Calibration Methods with Compensation of Geometric Distortions in Fringe Projection Profilometry: A Comparative Study

3D reconstruction of laser projective point with projection invariant generated from five points on 2D target

Object Detection and Tracking-Based Camera Calibration for Normalized Human Height Estimation

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