Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
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IEEE Computer Graphics and Applications
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GRIN'01 No description on Graphics interface 2001
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Multiresolution volume simplification and polygonization
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Selective Refinement Queries for Volume Visualization of Unstructured Tetrahedral Meshes
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Efficient representation and extraction of 2-manifold isosurfaces using kd-trees
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A Parallel Framework for Simplification of Massive Meshes
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Planet-Sized Batched Dynamic Adaptive Meshes (P-BDAM)
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Large Mesh Simplification using Processing Sequences
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Real-time mesh simplification using the GPU
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Fast low-memory streaming MLS reconstruction of point-sampled surfaces
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Real-time, dynamic level-of-detail management for three-axis NC milling simulation
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Distributed visibility culling technique for complex scene rendering
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Simplification algorithm for large polygonal model in distributed environment
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
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Out-of-core real-time visualization of massive 3D point clouds
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Fast reconstruction of 3d terrain model from contour lines on 2d maps
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Remote large data visualization in the paraview framework
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iRun: interactive rendering of large unstructured grids
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Parallel simplification of large meshes on PC clusters
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A dynamic surface reconstruction framework for large unstructured point sets
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Interactive out-of-core texturing with point-sampled textures
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Efficient and prioritized point subsampling for CSRBF compression
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SD Models: Super-Deformed Character Models
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ACM Transactions on Graphics (TOG)
Constructing isosurfaces with sharp edges and corners using cube merging
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
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We present an algorithm for out-of-core simplification of large polygonal datasets that are too complex to fit in main memory. The algorithm extends the vertex clustering scheme of Rossignac and Borrel [13] by using error quadric information for the placement of each cluster's representative vertex, which better preserves fine details and results in a low mean geometric error. The use of quadrics instead of the vertex grading approach in [13] has the additional benefits of requiring less disk space and only a single pass over the model rather than two. The resulting linear time algorithm allows simplification of datasets of arbitrary complexity.In order to handle degenerate quadrics associated with (near) flat regions and regions with zero Gaussian curvature, we present a robust method for solving the corresponding underconstrained least-squares problem. The algorithm is able to detect these degeneracies and handle them gracefully. Key features of the simplification method include a bounded Hausdorff error, low mean geometric error, high simplification speed (up to 100,000 triangles/second reduction), output (but not input) sensitive memory requirements, no disk space overhead, and a running time that is independent of the order in which vertices and triangles occur in the mesh.