DocumentCode
1551707
Title
Eigen-Texture method: appearance compression and synthesis based on a 3D model
Author
Nishino, Ko ; Sato, Yoichi ; Ikeuchi, Katsushi
Author_Institution
Dept. of Inf. Sci., Tokyo Univ., Japan
Volume
23
Issue
11
fYear
2001
fDate
11/1/2001 12:00:00 AM
Firstpage
1257
Lastpage
1265
Abstract
Image-based and model-based methods are two representative rendering methods for generating virtual images of objects from their real images. However, both methods still have several drawbacks when we attempt to apply them to mixed reality where we integrate virtual images with real background images. To overcome these difficulties, we propose a new method, which we refer to as the Eigen-Texture method. The proposed method samples appearances of a real object under various illumination and viewing conditions, and compresses them in the 2D coordinate system defined on the 3D model surface generated from a sequence of range images. The Eigen-Texture method is an example of a view-dependent texturing approach which combines the advantages of image-based and model-based approaches. No reflectance analysis of the object surface is needed, while an accurate 3D geometric model facilitates integration with other scenes. The paper describes the method and reports on its implementation
Keywords
data compression; eigenvalues and eigenfunctions; image sampling; image texture; principal component analysis; rendering (computer graphics); 2D coordinate system; 3D model surface; Eigen-Texture method; accurate 3D geometric model; appearance compression; appearance synthesis; illumination; image synthesis; mixed reality; model-based approaches; principle component analysis; range images; real background images; real object; rendering methods; view-dependent texturing approach; viewing conditions; virtual images; Image analysis; Image coding; Layout; Reflectivity; Rendering (computer graphics); Rough surfaces; Solid modeling; Surface roughness; Surface texture; Virtual reality;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/34.969116
Filename
969116
Link To Document