Area and volume coherence for efficient visualization of 3D scalar functions
VVS '90 Proceedings of the 1990 workshop on Volume visualization
Raytracing irregular volume data
VVS '90 Proceedings of the 1990 workshop on Volume visualization
A polygonal approximation to direct scalar volume rendering
VVS '90 Proceedings of the 1990 workshop on Volume visualization
A vectorized particle tracer for unstructured grids
Journal of Computational Physics
Volume visualization of 3D finite element method results
IBM Journal of Research and Development
Visibility-ordering meshed polyhedra
ACM Transactions on Graphics (TOG)
Direct volume visualization of three-dimensional vector fields
VVS '92 Proceedings of the 1992 workshop on Volume visualization
Tetrahedral mesh compression with the cut-border machine
VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
Out-of-core build of a topological data structure from polygon soup
Proceedings of the sixth ACM symposium on Solid modeling and applications
Efficient Streamline, Streamribbon, and Streamtube Constructions on Unstructured Grids
IEEE Transactions on Visualization and Computer Graphics
Out-of-Core Streamline Visualization on Large Unstructured Meshes
IEEE Transactions on Visualization and Computer Graphics
Volume Visualization of Sparse Irregular Meshes
IEEE Computer Graphics and Applications
Interactive splatting of nonrectilinear volumes
VIS '92 Proceedings of the 3rd conference on Visualization '92
Out-of-core encoding of large tetrahedral meshes
VG '03 Proceedings of the 2003 Eurographics/IEEE TVCG Workshop on Volume graphics
LoD Volume Rendering of FEA Data
VIS '04 Proceedings of the conference on Visualization '04
SOT: compact representation for tetrahedral meshes
2009 SIAM/ACM Joint Conference on Geometric and Physical Modeling
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Adjacency graphs of meshes are important for visualizing or compressing unstructured scientific data. However, calculating adjacency graphs requires intensive memory space. For large data sets, the calculation becomes very inefficient on desk-top computers with limited main memory. In this article, an out-of-core method is presented for finding connectivities of large unstructured FEA data sets. Our algorithm composes of three stages. At the first stage, FEA cells are read into main memory in blocks. For each cell block read, cell faces are generated and distributed into disjoint groups. These groups are small enough such that each group can reside in main memory without causing any page swapping. The resulted groups are stored in disk files. At the second stage, the face groups are fetched into main memory and processed there one after another. Adjacency graph edges are determined in each face group by sorting faces and examining consecutive faces. The edges contained in a group are kept in a disk file. At the third stage, edge files are merged into a single file by using external merge sort, and the connectivity information is computed.