Volume Rendering Using Principal Component Analysis

Alakkari, Salaheddin; Dingliana, John

Volume Rendering Using Principal Component Analysis

Files

085-087.pdf (671 KB)

poster_final.pdf (1.12 MB)

pca-video.mp4 (25.69 MB)

Date

2016

Authors

Alakkari, Salaheddin
Dingliana, John

Publisher

The Eurographics Association

Abstract

We investigate the use of Principal Component Analysis (PCA) for image-based volume rendering. We compute an eigenspace using training images, pre-rendered using a standard raycaster, from a spherically distributed range of camera positions. Our system is then able to synthesize novel views of the data set with minimal computation at run time. Results indicate that PCA is able to sufficiently learn the full volumetric model through a finite number of training images and generalize the computed eigenspace to produce high quality novel view images.

        @inproceedings{10.2312:eurp.20161148
,
booktitle = {EuroVis 2016 - Posters
},
editor = {Tobias Isenberg and Filip Sadlo
},
title = {{Volume Rendering Using Principal Component Analysis
}},
author = {Alakkari, Salaheddin and 
Dingliana, John
},
year = {2016
},
publisher = {The Eurographics Association
},
ISSN = {-
},
ISBN = {978-3-03868-015-4
},
DOI = {10.2312/eurp.20161148
}
}