Efficient Isosurface Extraction for Large Scale Time-Varying Data Using the Persistent Hyperoctree (PHOT)

We introduce the Persistent HyperOcTree (PHOT) to handle the4D isocontouring problem for large scale time-varying data
sets. This novel data structure is provably space
efficient and optimal in retrieving active cells. More
importantly, the set of active cells for any possible
isovalue are already organized in a Compact Hyperoctree, which
enables very efficient slicing of the isocontour along spatial
and temporal dimensions. Experimental results based
on the very large Richtmyer-Meshkov instability data set
demonstrate the effectiveness of our approach. This
technique can also be used for other isosurfacing schemes such
as view-dependent isosurfacing and ray-tracing,
which will benefit from the inherent hierarchical structure
associated with the active cells.