TY - JOUR
T1 - High-quality extraction of isosurfaces from regular and irregular grids
AU - Schreiner, John
AU - Scheidegger, Carlos E.
AU - Silva, Cláudio T.
N1 - Funding Information:
Our research is funded by the National Science Foundation (grants CCF-0401498, EIA-0323604, OISE-0405402, IIS-0513692, CCF-0528201), the Department of Energy, Sandia National Laboratories, Lawrence Livermore National Laboratory, an IBM Faculty Award, and a University of Utah Seed Grant.
PY - 2006/9
Y1 - 2006/9
N2 - Isosurfaces are ubiquitous in many fields, including visualization, graphics, and vision. They are often the main computational component of important processing pipelines (e.g., surface reconstruction), and are heavily used in practice. The classical approach to compute isosurfaces is to apply the Marching Cubes algorithm, which although robust and simple to implement, generates surfaces that require additional processing steps to improve triangle quality and mesh size. An important issue is that in some cases, the surfaces generated by Marching Cubes are irreparably damaged, and important details are lost which can not be recovered by subsequent processing. The main motivation of this work is to develop a technique capable of constructing high-quality and high-fidelity isosurfaces. We propose a new advancing front technique that is capable of creating high-quality isosurfaces from regular and irregular volumetric datasets. Our work extends the guidance field framework of Schreiner et al. to implicit surfaces, and improves it in significant ways. In particular, we describe a set of sampling conditions that guarantee that surface features will be captured by the algorithm. We also describe an efficient technique to compute a minimal guidance field, which greatly improves performance. Our experimental results show that our technique can generate high-quality meshes from complex datasets.
AB - Isosurfaces are ubiquitous in many fields, including visualization, graphics, and vision. They are often the main computational component of important processing pipelines (e.g., surface reconstruction), and are heavily used in practice. The classical approach to compute isosurfaces is to apply the Marching Cubes algorithm, which although robust and simple to implement, generates surfaces that require additional processing steps to improve triangle quality and mesh size. An important issue is that in some cases, the surfaces generated by Marching Cubes are irreparably damaged, and important details are lost which can not be recovered by subsequent processing. The main motivation of this work is to develop a technique capable of constructing high-quality and high-fidelity isosurfaces. We propose a new advancing front technique that is capable of creating high-quality isosurfaces from regular and irregular volumetric datasets. Our work extends the guidance field framework of Schreiner et al. to implicit surfaces, and improves it in significant ways. In particular, we describe a set of sampling conditions that guarantee that surface features will be captured by the algorithm. We also describe an efficient technique to compute a minimal guidance field, which greatly improves performance. Our experimental results show that our technique can generate high-quality meshes from complex datasets.
KW - Advancing front
KW - Curvature
KW - Isosurface extraction
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U2 - 10.1109/TVCG.2006.149
DO - 10.1109/TVCG.2006.149
M3 - Article
C2 - 17080853
AN - SCOPUS:33845671319
SN - 1077-2626
VL - 12
SP - 1205
EP - 1212
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
IS - 5
ER -