Camera Calibration for 3D Reconstruction and View Transformation

This chapter presents an extensive overview of passive camera calibration techniques. Starting with a detailed introduction and mathematical description of the imaging process of an off-the-shelf camera, it reviews all existing passive calibration approaches with increasing complexity. All algorithms are presented in detail so that they are directly applicable. For completeness, a brief counting about the self-calibration is also provided. In addition, two typical applications are given of passive camera calibration methods for specific problems of face model reconstruction and telepresence and experimentally evaluated. It is expected that this chapter can serve as a standard reference. Researchers in various fields in which passive camera calibration is actively or potentially of interest can use this chapter to identify the appropriate techniques suitable for their applications.

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A Transformational Approach to Explicit Stereo Camera Calibration for Improved Euclidean Accuracy of Infrastructure 3D Reconstruction

Computing in Civil Engineering ◽

10.1061/9780784413029.089 ◽

2013 ◽

Cited By ~ 2

Author(s):

H. Fathi ◽

I. Brilakis

Keyword(s):

3D Reconstruction ◽

Camera Calibration ◽

Stereo Camera

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An Affine Invariant of Parallelograms and Its Application to Camera Calibration and 3D Reconstruction

Computer Vision – ECCV 2006 - Lecture Notes in Computer Science ◽

10.1007/11744047_15 ◽

2006 ◽

pp. 191-204 ◽

Cited By ~ 3

Author(s):

F. C. Wu ◽

F. Q. Duan ◽

Z. Y. Hu

Keyword(s):

3D Reconstruction ◽

Camera Calibration ◽

Affine Invariant

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Camera Calibration for Underwater 3D Reconstruction Based on Ray Tracing Using Snell’s Law

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) ◽

10.1109/cvprw.2018.00190 ◽

2018 ◽

Cited By ~ 4

Author(s):

Malte Pedersen ◽

Stefan Hein Bengtson ◽

Rikke Gade ◽

Niels Madsen ◽

Thomas B. Moeslund

Keyword(s):

3D Reconstruction ◽

Ray Tracing ◽

Camera Calibration ◽

Snell’S Law ◽

Snell's Law

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Time-of-Flight Camera Calibration for Improved 3D Reconstruction of Indoor Scenes

2014 Seventh International Symposium on Computational Intelligence and Design ◽

10.1109/iscid.2014.283 ◽

2014 ◽

Author(s):

Meng Xie ◽

Jeremy R. Cooperstock

Keyword(s):

3D Reconstruction ◽

Camera Calibration ◽

Time Of Flight ◽

Indoor Scenes

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Acquisition of Point Cloud Data in 3D Reconstruction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.536-537.213 ◽

2014 ◽

Vol 536-537 ◽

pp. 213-217

Author(s):

Meng Qiang Zhu ◽

Jie Yang

Keyword(s):

3D Reconstruction ◽

Camera Calibration ◽

Point Cloud ◽

Corner Detection ◽

Projection Mapping ◽

Cloud Data ◽

3D Space ◽

Dense Point ◽

Left And Right ◽

Object Depth

This paper takes the following measures to solve the problem of 3D reconstruction. Camera calibration is based on chessboard, taking several different attitude images. Use corner point coordinates by corner detection to process camera calibration. The calibration result is important to be used to correct the distorted image. Next, the left and right images should be matched to find out the object surface points’ imaging position respectively so that the object depth can be calculated by triangulation. According to the inverse process of projection mapping, we can project the object depth and disparity information into 3D space. As a result, we can obtain dense point cloud, which is ready for 3D reconstruction.

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Three-Dimensional Object Surface Reconstruction Based on Camera Calibration and SIFT

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.719-720.1191 ◽

2015 ◽

Vol 719-720 ◽

pp. 1191-1197 ◽

Cited By ~ 1

Author(s):

Jun Zhang ◽

Long Ye ◽

Qin Zhang ◽

Jing Ling Wang

Keyword(s):

3D Reconstruction ◽

Camera Calibration ◽

Image Matching ◽

Three Dimensional ◽

Target Object ◽

Object A ◽

Binocular Stereo Vision ◽

Binocular Stereo ◽

Dimensional Object

This paper is focused on camera calibration, image matching, both of which are the key issues in three-dimensional (3D) reconstruction. In terms of camera calibration firstly, we adopt the method based on the method proposed by Zhengyou Zhang. In addition to this, it is selective for us to deal with tangential distortion. In respect of image matching, we use the SIFT algorithm, which is invariant to image translation, scaling, rotation, and partially invariant to illumination changes and to affine or 3D projections. It performs well in the follow-up matching the corresponding points. Lastly, we perform 3D reconstruction of the surface of the target object. A Graphical User Interface is designed to help us to realize the key function of binocular stereo vision, with better visualization. Apparently, the entire GUI brings convenience to the follow-up work.

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Evaluation of Solving Methods for the Fundamental Matrix Computation

Journal of Computational Vision and Imaging Systems ◽

10.15353/jcvis.v6i1.3563 ◽

2021 ◽

Vol 6 (1) ◽

pp. 1-1

Author(s):

Katherine Arnold ◽

Mohamed A. Naiel ◽

Mark Lamm ◽

Paul Fieguth

Keyword(s):

3D Reconstruction ◽

Camera Calibration ◽

Fundamental Matrix ◽

Synthetic Data ◽

Ground Truth ◽

Matrix Computation ◽

Linear Solvers ◽

Non Linear ◽

Measurements Errors ◽

Image Calibration

Solving the fundamental matrix is a key step in many image calibration and 3D reconstruction systems. The goal of this paper is to study the performance of non-linear solvers for estimating the fundamental matrix in projector-camera calibration. To prevent measurements errors from distorting our understanding, synthetic data are created from ground-truth camera and projector parameters and then used for the assessment of four nonlinear solving strategies.

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