Progress and Review of 3D Image Feature Reconstruction

Title:Progress and Review of 3D Image Feature Reconstruction

Volume: 10 Issue: 1

Author(s): Gaiyun Zhou*, Haifeng Hu and Li Ma

Affiliation:

• College of Software, Pingdingshan University, Henan Pingdingshan,China

Keywords: 3D image feature reconstruction, surface rending technology, volume rending technology, stereo vision matching technology, convergence speed.

Abstract: Background: Three dimensional image feature reconstruction is to establish a mathematical model, which is suitable for computer representation and processing. It is the basis of processing, operating and analyzing its properties in computer environment, as described in various patents.

Method: This paper introduces the present situation of the research on the reconstruction of 3D image features, and analyzes the method of 3D image reconstruction. 3D feature image reconstruction is an important branch of 3D image processing technology. Through the principle analysis and technology research of 3D image reconstruction technology, the development status of the relevant research at home and abroad is made detailed analysis. The different methods for surface rending technology, volume rending technology, and stereo vision matching technology are analyzed; in addition, the working principles and the existing problems are analyzed.

Result: Experiments have proved the performance of the correlation method, e volume rendering technology can remodel real three-dimensional shape of the corn leaf better, the ups and downs of edge of the corn leaf and drape, other details can be preserved, effectively improve the convergence speed, and greatly reduce the running time and the number of iterations.

Conclusion: It provides the development direction for the 3D image feature reconstruction.

Cite this article as:

Zhou Gaiyun*, Hu Haifeng and Ma Li, Progress and Review of 3D Image Feature Reconstruction, Recent Patents on Computer Science 2017; 10 (1) . https://dx.doi.org/10.2174/2213275909666161025150732

DOI https://dx.doi.org/10.2174/2213275909666161025150732	Print ISSN 2213-2759
Publisher Name Bentham Science Publisher	Online ISSN 1874-4796