›› 2010, Vol. 25 ›› Issue (3): 606-613.

Special Issue: Artificial Intelligence and Pattern Recognition

• Special Section on Trends Changing Data Management • Previous Articles     Next Articles

Geometry Texture Synthesis Based on Laplacian Texture Image

Ling-Qiang Ran (冉令强) and Xiang-Xu Meng (孟祥旭), Senior Member, CCF   

  1. School of Computer Science and Technology, Shandong University, Jinan 250101, China
  • Received:2009-07-09 Revised:2010-02-25 Online:2010-05-05 Published:2010-05-05
  • About author:
    Ling-Qiang Ran is currently pursuing the Ph.D. degree in the School of Computer Science and Technology, Shandong University, Jinan, China. He received his B.S. degree from Software College, Shandong University in 2008. His research interests include computer graphics and digital geometry processing.
    Xiang-Xu Meng received his B.Sc. and M.Eng. degrees in Dept. Computer Science of Shandong University in 1982 and 1985 respectively. He received his Ph.D. degree from the Institute of Computing Technology, Chinese Academy of Sciences in 1998. He is a professor in the School of Computer Science and Technology at Shandong University. His research interests include human-computer interaction, virtual reality, computer graphics, CAD/CAM/CIMS, grid computing, visualization and scientific computing. He is a senior member of China Computer Federation.
  • Supported by:

    Supported by the National Basic Research 973 Program of China under Grant No. 2006CB303102 and the National Natural Science Foundation of China under Grant No. 60703028.

In this paper, we present a new method to synthesize geometric texture details on an arbitrary surface from a sample texture patch. The key idea is to use Laplacian texture images to represent geometric texture details, which in turn facilitate simple and effective geometry texture synthesis and enable flexible geometry texture editing. Given a sample model and a target model, we first select a patch from the sample model and extract the geometric texture details. Next, we construct a Laplacian texture image for the extracted texture and synthesize the Laplacian texture image to the target model using an image texture synthesis technique on surface. Finally, we reconstruct the textured target model with adjusted Laplacian coordinates. Compared to the existing methods, our method is easy-to-implement and produces results of high quality. Furthermore, it performs flexible control on the appearance of the textured target model through operations on the Laplacian texture image.


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