T. K. Dey, J. Giesen, J. Hudson, Delaunay based shape reconstruction from large data, in: Proc. IEEE Symposium in Parallel and Large Data Visualization and Graphics, 2001, pp. 19--27. Home/Search Document Details and Download
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Filling Holes in Complex Surfaces using Volumetric Diffusion - Davis, Marschner, Garr.. (2002) (15 citations) (Correct)
....range samples, the large gap across a hole is conceptually equivalent to the space between adjacent samples, so these methods effectively fill holes during reconstruction. One class of point cloud methods interpolates the original samples using alpha shapes [Edelsbrunner92, Bajaj95] crusts [Amenta98, Dey01], or balls [Bernardini99 ] However, interpolation may not be appropriate for noisy data, and these algorithms may fail if sample noise approaches sample density which it often does. Also, in algorithms based on alpha shapes or balls, it may be difficult to find a single alpha (or ball radius) ....
Dey, T.K., Giesen, J., Hudson, J., "Delaunay Based Shape Reconstruction from Large Data," Proc. Symposium on Parallel and Large Data Visualization and Graphics Surfaces and Parallel Rendering" , ACM, October, 2001. 10
CGAL - The Computational Geometry Algorithm Library - Fabri (2001) (1 citation) (Correct)
....for many very di#erent application areas. Here are some examples for how Cgal data structures get used: 3D regular triangulation for transition mesh generation in geological modelling [2] 3D Delaunay triangulation for coarse grained molecular dynamics [10] and for surface reconstruction [8], 2D Delaunay triangulations for cell decomposition in air tra#c control, polyhedral surfaces for surface extraction from Mris, smallest enclosing spheres for fast collision detection in games, boolean operations on polygons for segmentation algorithms in imaging, arrangements of arcs of circles ....
T.K. Dey, J .Giesen & J. Hudson. Delaunay based shape reconstruction from large data. Proc. IEEE Symposium in Parallel and Large Data Visualization and Graphics, 2001.
Undersampling and Oversampling in Sample Based Shape.. - Dey, Giesen, Goswami, .. Self-citation (Dey Giesen Hudson) (Correct)
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T. K. Dey, J. Giesen and J. Hudson. A Delaunay based shape reconstruction from large data. Proc. IEEE Sympos. in Parallel and Large Data Visualization and Graphics, (2001), to appear.
PMR: Point to Mesh Rendering, A Feature-Based Approach - Dey, Hudson Self-citation (Dey Hudson) (Correct)
....the computation. We overcome this problem by dividing the data with an octree and then concentrating on each node individually for the Voronoi diagram and subsequent computations. This strategy has given remarkable success in Voronoi based surface reconstruction from large point clouds, see [10] for details. The depth of the octree subdivision depends on the number of points that we allow in the octree nodes at the lowest level (leaf nodes) Since these octree nodes will be used later in the rendering stage as well, it is advantageous that they not have too many or too few points within ....
T. K. Dey, J. Giesen and J. Hudson. A Delaunay based shape reconstruction from large data. Proc. IEEE Sympos. in Parallel and Large Data Visualization and Graphics, (2001), 19--27.
How to add facet attributes to Cgal's 3D geometric triangulations - Giesen, John Self-citation (Giesen) (Correct)
....implementations of geometric algorithms and data structures. One of its powerful features is the triangulation package that includes Delaunay and regular triangulations. We were involved in the development and implementation of three di erent surface reconstruction algorithms and their extensions [1, 5, 7, 9]. Additionally,we implemented prototypes of several other surface reconstruction algorithms [2, 3, 10] The task in the surface reconstruction problem is to compute a manifold triangular mesh from a set of unorganized points that are sampled from the surface of some solid in R . Figure 1 shows ....
T.K. Dey, J. Giesen, J. Hudson. Delaunay Based Shape Reconstruction from Large Data, In IEEE Symposium in Parallel and Large Data Visualization and Graphics, pp. 19-27, (2001)
Decimating Samples for Mesh Simplification - Dey, Giesen, Hudson (2001) (4 citations) Self-citation (Dey Giesen Hudson) (Correct)
....generates a dense set of discrete samples. Surface reconstruction is the problem of interpolating these samples with a piecewise linear surface that approximates the original surface. A number of algorithms with various guarantees and capabilities have been designed in recent years for the problem [1, 2, 3, 4, 5, 6, 7, 11, 14, 15, 17, 19]. Reconstructed surfaces are used for various purposes such as display in computer graphics, finite element methods in physical simulations or in computer aided manufacturing. Often the surface becomes unwieldy for these postprocessings because of their large combinatorial description. Naturally, ....
T. K. Dey, J. Giesen and J. Hudson. A Delaunay based shape reconstruction from large data. Proc. IEEE Sympos. in Large Data Visualization and Graphics, (2001), to appear.
Undersampling and Oversampling in Sample Based Shape.. - Dey, Giesen, Goswami, .. Self-citation (Dey Giesen Hudson) (Correct)
....The surface patches from different cells are stitched automatically by manifold extraction. This result shows that COCONE can be extended to handle data even with more than a million points in less than an hour using modest computing resource such as PCs. The details appear in a companion paper [6]. Noise is the other challenge that needs further investigation. The noise created in the form of outliers is easy to detect and COCONE can easily isolate them. However, local noise introduced by small perturbations in the coordinates of the sample points causes difficulty with the current state ....
T. K. Dey, J. Giesen and J. Hudson. A Delaunay based shape reconstruction from large data. Proc. IEEE Sympos. in Parallel and Large Data Visualization and Graphics, (2001), to appear.
Cycle Bases of Graphs and Sampled - Manifolds Craig Gotsman (Correct)
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T. K. Dey, J. Giesen, J. Hudson, Delaunay based shape reconstruction from large data, in: Proc. IEEE Symposium in Parallel and Large Data Visualization and Graphics, 2001, pp. 19--27.
Reinforcement Learning in the Joint Space: Value Iteration in.. - Monson (2003) (Correct)
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T. Dey, J. Giesen, and J. Hudson, A Delaunay based shape reconstruction from large data, Proceedings of the IEEE Symposium in Parallel and Large Data Visualization and Graphics (San Diego, California), IEEE Press, 2001, pp. 19--27.
Reinforcement Learning in the Joint Space: Value Iteration in.. - Monson (2003) (Correct)
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T. Dey, J. Giesen, and J. Hudson, A Delaunay based shape reconstruction from large data, Proceedings of the IEEE Symposium in Parallel and Large Data Visualization and Graphics (San Diego, California), IEEE Press, 2001, pp. 19--27.
Approximating and Intersecting Surfaces from Points - Adamson, Alexa (2003) (3 citations) (Correct)
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T. K. Dey, J. Giesen, and J. Hudson. Delaunay based shape reconstruction from large data. In IEEE Symposium on Parallel and Large Data Visualization, pages 19--27, 2001.
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