NDLI: Capture and Synthesis of 3D Surface Texture

Content Provider	Springer Nature Link
Author	Dong, Junyu Chantler, Mike
Copyright Year	2005
Abstract	We present and compare five approaches for capturing, synthesising and relighting real 3D surface textures. Unlike 2D texture synthesis techniques they allow the captured textures to be relit using illumination conditions that differ from those of the original. We adapted a texture quilting method due to Efros and combined this with five different relighting representations, comprising: a set of three photometric images; surface gradient and albedo maps; polynomial texture maps; and two eigen based representations using 3 and 6 base images.We used twelve real textures to perform quantitative tests on the relighting methods in isolation. We developed a qualitative test for the assessment of the complete synthesis systems. Ten observers were asked to rank the images obtained from the five methods using five real textures. Statistical tests were applied to the rankings.The six-base-image eigen method produced the best quantitative relighting results and in particular was better able to cope with specular surfaces. However, in the qualitative tests there were no significant performance differences detected between it and the other two top performers. Our conclusion is therefore that the cheaper gradient and three-base-image eigen methods should be used in preference, especially where the surfaces are Lambertian or near Lambertian.
Starting Page	177
Ending Page	194
Page Count	18
File Format	PDF
ISSN	09205691
Journal	International Journal of Computer Vision
Volume Number	62
Issue Number	1-2
e-ISSN	15731405
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2005-01-01
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Artificial Intelligence (incl. Robotics) Computer Imaging, Graphics and Computer Vision Image Processing Automation and Robotics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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