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Fast Volume Rendering and Data Classification Using Multiresolution in Min‐Max Octrees
Author(s) -
Dong Feng,
Krokos Meleagros,
Clapworthy Gordon
Publication year - 2000
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/1467-8659.00428
Subject(s) - rendering (computer graphics) , computer science , volume rendering , tiled rendering , computer graphics (images) , multiresolution analysis , alternate frame rendering , parallel rendering , real time rendering , data structure , software rendering , artificial intelligence , image resolution , texture memory , computer vision , graphics , wavelet , 3d computer graphics , discrete wavelet transform , wavelet transform , programming language
Large‐sized volume datasets have recently become commonplace and users are now demanding that volume‐rendering techniques to visualise such data provide acceptable results on relatively modest computing platforms. The widespread use of the Internet for the transmission and/or rendering of volume data is also exerting increasing demands on software providers. Multiresolution can address these issues in an elegant way. One of the fastest volume‐rendering alrogithms is that proposed by Lacroute & Levoy 1 , which is based on shear‐warp factorisation and min‐max octrees (MMOs). Unfortunately, since an MMO captures only a single resolution of a volume dataset, this method is unsuitable for rendering datasets in a multiresolution form. This paper adapts the above algorithm to multiresolution volume rendering to enable near‐real‐time interaction to take place on a standard PC. It also permits the user to modify classification functions and/or resolution during rendering with no significant loss of rendering speed. A newly‐developed data structure based on the MMO is employed, the multiresolution min‐max octree, M 3 O, which captures the spatial coherence for datasets at all resolutions. Speed is enhanced by the use of multiresolution opacity transfer functions for rapidly determining and discarding transparent dataset regions. Some experimental results on sample volume datasets are presented.