We describe an adaptive isosurface visualization scheme designed for perspective rendering, taking into account the center of interest and other viewer-specified parameters. To hierarchically decompose a given data domain into a multiresolution data structure, we implement and compare two spatial subdivision data structures: an octree and a recursively defined tetrahedral mesh based on longest-edge bisection. We have implemented our view-dependent isosurface visualization approach using both data structures -- comparing storage requirements, computational efficiency, and visual quality.

We extend the notion of multiresolution spatial data approximation of static datasets to spatio-temporal approximation of time-varying datasets. By including a temporal component, we allow a region from one time-step to approximate a congruent region at another time-step. While approximations of static datasets are generated by refining the representation if a given error-bound is not met, for approximations of time-varying datasets we use data from another time-step, if that data meets a given error-bound for the current time-step. Thus, our technique exploits the fact that time-varying datasets typically do not change uniformly over time, and by only loading data from rapidly changing regions, much less data need be loaded to generate an approximation. Regions that don't change, or change slowly, are not loaded and are approximated by regions from another time-step. Prior techniques only permit binary classifications between consecutive time-steps. Our technique allows a run-time error-criterion between non-temporally consecutive time-steps. Error between time-steps is calculated in a pre-processing step and stored in error-tables. These error-tables are used to calculate error at run-time, so no data is touched. User movements in time are not restricted to unidirectional, successive changes - movement can be forward, backward, or to any arbitrary time-step.