Paper Info
Title
Conversions between three methods for representing 3D surface textures under arbitrary illumination directions
Abstract
Representing the appearances of surfaces illuminated from different directions has long been an active research topic. While many representation methods have been proposed, the relationships and conversion between different representations have been less well researched. These relationships are important, as they provide (a) an insight as to the different capabilities of the surface representations, and (b) a means by which they may be converted to common formats for computer graphic applications. In this paper, we introduce a single mathematical framework and use it to express three commonly used surface texture relighting representations: surface gradients (Gradient), Polynomial Texture Maps (PTM) and eigen base images (Eigen). The framework explicitly reveals the relations between the three methods, and from this we propose a set of conversion methods. We use 26 rough surface textures illuminated from 36 directions for our experiments and perform both quantitative and qualitative assessments to evaluate the conversion methods. The quantitative assessment uses a normalized root-mean-squared error as metric to compare the original images and those produced by proposed representation methods. The qualitative assessment is based on psychophysical experiments and non-parametric statistics. The results from the two assessments are consistent and show that the original Eigen representation produces the best performance. The second best performances are achieved by the original PTM representation and the conversion between Polynomial Texture Maps (PTM) and eigen base images (Eigen), while the performances of other representations are not significantly different.
Year
DOI
Venue
2008
10.1016/j.imavis.2008.02.002
Image Vision Comput.
Keywords
Field
DocType
conversion methods,relighting,polynomial texture maps,surface texture,eigen,qualitative assessment,best performance,conversion method,original eigen representation,original ptm representation,different direction,polynomial texture map,gradient,different capability,different representation,arbitrary illumination direction,eigen base image,texture mapping,non parametric statistics,root mean square error,computer graphic
Texture mapping,Computer vision,Normalization (statistics),Pattern recognition,Polynomial,Artificial intelligence,Surface finish,Quantitative assessment,Mathematics
Journal
Volume
Issue
ISSN
26
12
Image and Vision Computing
Citations 
PageRank 
References 
3
0.38
17
Authors
3
Name
Order
Citations
PageRank
Junyu Dong139377.68
Guimei Sun251.10
Guojiang Chen330.72