Dataflow and remapping for wavelet compression and view dependent optimization of billion triangle isosurfaces

Abstract

Currently, large physics simulations produce 3D discretized field data whose individual isosurfaces, after conventional extraction processes, contain upwards of hundreds of millions of triangles. Detailed interactive viewing of these surfaces requires (a) powerful compression to minimize storage, and (b) fast view-dependent optimization of display triangulations to most effectively utilize high-performance graphics hardware. In this work, we introduce the first end-to-end multiresolution dataflow strategy that can effectively combine the top performing subdivision-surface wavelet compression and view-dependent optimization methods, thus increasing efficiency by several orders of magnitude over conventional processing pipelines. In addition to the general development and analysis of the dataflow, we present new algorithms at two steps in the pipeline that provide the âglueâ that makes an integrated large-scale data visualization approach possible. A shrink-wrapping step converts highly detailed unstructured surfaces of arbitrary topology to the semi-structured meshes needed for wavelet compression. Remapping to triangle bintrees minimizes disturbing âpopsâ during realtime displaytriangulation optimization and provides effective selective-transmission compression for out-of-core and remote access to extremely large surfaces. Overall, this is the first effort to exploit semi-structured surface representations for a complete large-data visualization pipeline.