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@InCollection{Erkoc_Et_Al_NUMGRID_2020,
  author="Erko{\c c}, Ziya and Aman, Aytek and G{\"u}d{\"u}kbay, U{\^g}ur and Si, Hang",
  editor="Garanzha, Vladimir A. and Kamenski, Lennard and Si, Hang",
  title="Out-of-core Constrained Delaunay Tetrahedralizations for Large Scenes",
  booktitle="Numerical Geometry, Grid Generation and Scientific Computing",
  series = {NUMGRID '20},
  year="2021",
  publisher="Springer International Publishing",
  address="Cham",
  pages="113--124",
  abstract="Tetrahedralization algorithms are used for many applications such as Ray Tracing and 
            Finite Element Methods. For most of the applications, constrained tetrahedralization 
			algorithms are chosen because they can preserve input triangles. The constrained 
			tetrahedralization algorithms developed so far might suffer from a lack of memory. 
			We propose an out-of-core near Delaunay constrained tetrahedralization algorithm 
			using the divide-and-conquer paradigm to decrease memory usage. If the expected 
			memory usage is below the user-defined memory limit, we tetrahedralize using TetGen. 
			Otherwise, we subdivide the set of input points into two halves and recursively apply
			the same idea to the two halves. When compared with the TetGen, our algorithm 
			tetrahedralizes the point clouds using less amount of memory but takes more time and 
			generates tetrahedralizations that do not satisfy the Delaunay criterion at the 
			boundaries of the merged regions. We quantify the error using the aspect-ratio metric. 
			The difference between the tetrahedralizations that our approach produce and the 
			Delaunay tetrahedralization are small and the results are acceptable for most applications.",
  isbn="978-3-030-76798-3",
  bib2html_dl_pdf = "http://www.cs.bilkent.edu.tr/~gudukbay/publications/papers/conf_papers/Erkoc_Et_Al_NUMGRID_2020.pdf",
  bib2html_pubtype = {Refereed Conference Papers},
  bib2html_rescat = {Computer Graphics}, 
}