Fast Volume Rendering and Data Classification Using Multiresolution Min-Max Octrees

摘要

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 algorithms 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 from. 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~3O, 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.