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Abstract:
We propose a new approach to efficiently render large volumetric data sets. The
system achieves interactive to real-time rendering performance for several
billion voxels.
Our solution is based on an adaptive data representation depending on the
current view and occlusion information, coupled to an efficient ray-casting
rendering algorithm. One key element of our method is to guide data production
and streaming directly based on information extracted during rendering.
Our data structure exploits the fact that in CG scenes, details are often
concentrated on the interface between free space and clusters of density and
shows that volumetric models might become a valuable alternative as a rendering
primitive for real-time applications.
In this spirit, we allow a quality/performance trade-off and exploit temporal
coherence. We also introduce a mipmapping-like process that allows for an
increased display rate and better quality through high quality filtering.
To further enrich the data set, we create additional details through a variety
of procedural methods.
We demonstrate our approach in several scenarios, like the exploration of a 3D
scan ($8192^3$ resolution),
of hypertextured meshes ($16384^3$ virtual resolution), or
of a fractal (theoretically infinite resolution).
All examples are rendered on current generation hardware at 20-90 fps and
respect the limited GPU memory budget.
