Acquisition of Point Cloud Data in 3D Reconstruction

Meng Qiang Zhu; Jie Yang

doi:10.4028/www.scientific.net/AMM.536-537.213

Paper Titles

Real-Time Information Processing Method and its Application in Optical Target Tracking System
p.192

Target Recognition Application of Real-Time Optical Information Processing System
p.197

The Initial Retrieval Based on Image Segmentation
p.201

Online Pedestrian Tracking with Kalman Filter and Random Ferns
p.205

Acquisition of Point Cloud Data in 3D Reconstruction
p.213

3-Dimensional Brain MRI Segmentation Based on Multi-Layer Background Subtraction and Seed Region Growing Algorithm
p.218

Application of an Improved Algorithm for Denoising of Wavelet Threshold in Palmprint Recognition
p.222

Association Degree Optimization Model and Algorithm for the Reconstruction of Cross-Cut Shredded Paper
p.226

Extraction Algorithm of Lip Characteristic Parameters Based on Interpolation
p.235

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 536-537Acquisition of Point Cloud Data in 3D...

Acquisition of Point Cloud Data in 3D Reconstruction

Abstract:

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.

You might also be interested in these eBooks

Advances in Mechatronics, Robotics and Automation II

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 536-537)

Pages:

213-217

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.536-537.213

Citation:

Cite this paper

Online since:

April 2014

Authors:

Meng Qiang Zhu*, Jie Yang

Keywords:

3D Reconstruction, Camera Calibration, Depth, Disparity, Point Cloud, Stereo Matching

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Xiaoxia Chen, Xiaolian Chen and Dekuan Liu. 3D image reconstruction process and achieve based on images(In Chinese Guizhou 2010).

Google Scholar

[2] Li Ma. 3D reconstruction technique research based on binocular vision(In Chinese Shanxi 2012).

Google Scholar

[3] Zhengyou Zhang. A Flexible New Technique for Camera Calibration(USA 1998).

Google Scholar

[4] Information on http: /blog. csdn. net/chenyusiyuan/article/details/5967291.

Google Scholar

[5] M. Pollefeys, D. Nister. Int J Comput, 2008, 78: 143-167.

Google Scholar

[6] Zhengyou Zhang. A Flexible New Technique for Camera Calibration. USA, 1998. 2-19.

Google Scholar

[7] E. Mouragnon, M. Lhuillier. etc. Real Time Localization and 3D Reconstruction. Computer Society Conference, 2006, 06: 1-7.

DOI: 10.1109/cvpr.2006.236

Google Scholar

[8] Zhu, Z. Y; Zhang, S; Chan, S. C ; Shum, H.Y. Object-based rendering and 3D reconstruction using a moveable image-based rendering system. Poitiers. 2011. 1-4.

DOI: 10.1109/nds.2011.6076844

Google Scholar

[9] Geiget, A; Ziegler, J; Stiller, C. Dense 3d reconstruction in real-time. Baden, 2011. 963-968.

Google Scholar