Method on 3D Dense Point Cloud Recovery of Geographical Scene

Yan Liang; Ye Hua Sheng; Ka Zhang

doi:10.4028/www.scientific.net/AMR.748.619

Paper Titles

The Research on Multiscale Image Fusion Based on the Dualtree Complex Wavelet
p.600

Signal Analysis on Nasal Consonant Based on Nasal Flow Signal
p.605

Gesture Recognition for Briefing Application
p.610

Optimization Algorithms Used in Reservoir History Matching
p.614

Method on 3D Dense Point Cloud Recovery of Geographical Scene
p.619

A Multi-Gradation Stereo Matching Method Based on Edge Feature Points
p.624

Implementation of Unitary Music Algorithm Using Xilinx System Generator
p.629

Sound Source Localization Based on Microphone Uniform Linear Array
p.634

Variable-Number Differential Evolution and its Application on Uranium Ore Classification
p.640

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 748Method on 3D Dense Point Cloud Recovery of...

Method on 3D Dense Point Cloud Recovery of Geographical Scene

Abstract:

The object of this research is to reconstruct 3D dense point cloud of geographical scene. With the technology and method of computer vision , first affine invariant features are extracted and matched, then cameras parameters and 3D dense point cloud are recovered and united under geographical reference. The experimental results show that this method with low cost and high precision of centimeters can satisfy the requirements of measurement, modeling and virtual reality.

You might also be interested in these eBooks

Material and Manufacturing Technology IV

View Preview

Info:

Periodical:

Advanced Materials Research (Volume 748)

Pages:

619-623

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.748.619

Citation:

Cite this paper

Online since:

August 2013

Authors:

Yan Liang, Ye Hua Sheng, Ka Zhang

Keywords:

Affine Invariant, Geographical Scene, Point Cloud, SFM

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Yasutaka Furukawa and Jean Ponce, "Accurate, Dense, and Robust Multi-View Stereopsis," Computer Vision and Pattern Recognition, Vol.1, pp.1-8, June 2007.

DOI: 10.1109/cvpr.2007.383246

Google Scholar

[2] Noah Snavely · Steven M. Seitz · Richard Szeliski, "Modeling the World from Internet Photo Collections", International Journal of Computer Vision, Vol.80, pp.189-210, 2008.

DOI: 10.1007/s11263-007-0107-3

Google Scholar

[3] Yongjun Zhang, Zuxun Zhang, Jianqing Zhang, Jun Wu, "3D Building Modelling with Digital Map, Lidar Data and Video Image Sequences" The Photogrammetric Record, Vol 20(111): pp.285-302, September 2005.

DOI: 10.1111/j.1477-9730.2005.00316.x

Google Scholar

[4] Li Long, He Ming-yi, Dai Yu-chao. "Three Dimensional Measurement Based on Multi-View Uncalibrated Images" MICROELECTRONICS&COMPUTER Vol.27, pp.181-185, September, 2010.

Google Scholar

[5] LANG Hai-tao, WANG Yong-tian, LIU Yue, WU Lun. "A Dense Reconstruction Algorithm Based on Uncalibrated Images" Journal of System Simulation Vol.20, pp.363-366, September, 2008.

Google Scholar

[6] CHE Xiang-jiu, Lu Yang, ZHAO Yi-wu, CHE Na, GAO Zhan-heng. "Simplification Implementation of Scattered Point Cloud Based on the Density in k-Nearest Neighbors" Journal of Jilin University (Scinece Edition), Vol 47, pp.994-998 September, 2009.

Google Scholar