1 / 31

CUBIT Research at the University of South Florida

Presented by Eugene Fink. CUBIT Research at the University of South Florida. Graduate students Shiraj Khan Matt Boonstra Undergrad students Kevin Albrecht Plamen Stoyanov Will Frost. Faculty Sunil Saigal Eugene Fink. People. CUBIT team Steve Owen Byron Hanks. Shiraj Khan.

kedem
Download Presentation

CUBIT Research at the University of South Florida

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Presented byEugene Fink CUBIT Research at the University of South Florida

  2. Graduate students Shiraj Khan Matt Boonstra Undergrad students Kevin Albrecht Plamen Stoyanov Will Frost Faculty Sunil Saigal Eugene Fink People CUBIT team Steve Owen Byron Hanks

  3. Shiraj Khan Kevin Albrecht Plamen Stoyanov Will Frost Matt Boonstra Tasks • Surface flattening • Spatial indexing • Format conversion • Code cleanup

  4. Tasks • Surface flattening • Spatial indexing • Format conversion • Code cleanup

  5. z y x v , angles • Preserve lengths , and areas u Surface flattening • Problem: • Map a faceted sur- face into the plane

  6. Surface flattening • Methods: • Orthogonal projection, FacetProjParamTool • RoadKill (by Alla Sheffer), FacetParamTool • RoadKill with hole patching, FacetParamTool

  7. Projection Flatten a faceted surface byprojecting it onto a plane. z y x

  8. Project onto this plane v • Else, convert the projection into two coordinates u Projection • Find the best-fit plane z y • If there are overlaps, then report a failure x Drawback: Works only for near-flat surfaces.

  9. RoadKill An algorithm for flattening faceted surfaces,by Alla Sheffer and Eric de Sturler (2001). • Minimizes the deformation of angles • Uses Newton’s method to solve a constrained minimization problem Drawback: Works only for surfaces without holes.

  10. Patching holes Close all holes andthen apply RoadKill. z y x

  11. Triangulate the resulting projection • Map the triangulation back onto the surface Patching holes • Project a hole onto the best-fit plane z y x Drawback: Works only for near-flat holes.

  12. RoadKill (by Alla Sheffer) • RoadKill with hole patching Examples • Orthogonal projection

  13. z y • Cracking faceted surfaces x v u Future extensions • Patching complex holes

  14. Tasks • Surface flattening • Spatial indexing • Format conversion • Code cleanup

  15. Addition of new objects • Deletion of objects • Retrieval of the objects that intersect a given object • Retrieval of the nearest neighbors Spatial indexing Problem: Indexing and retrieval ofobjects in three dimensions.

  16. Spatial indexing • Methods: • Previous: R-trees (Guttman, 1984), RTree • Current:KD-trees (Bentley, 1975), KDDTree • Future: R*-trees (Beckmann et al.,1990), RStarTree

  17. f • This tree also allows: • Indexing of boxes by their center points b d a c e • Indexing of arbitrary objects by their bounding boxes a b d c e f KD-trees A binary tree for indexing ofpoints in multiple dimensions.

  18. KD-trees • Advantages: • Fast initial construction • Fast retrieval of points • Drawbacks: • Slow insertion • Slow deletion Performance in CUBIT: KD-trees are usually faster than R-trees.

  19. Retrieval of points 30 10 R-trees milliseconds 3 KD-trees 1 1,000 100,000 10,000 number of facets Performance Initial construction 30 10 seconds R-trees 3 KD-trees 1 1,000 100,000 10,000 number of facets • KD-trees are faster than R-trees: • Construction is about 3 times faster • Retrieval is about 1.5 times faster

  20. Future extensions • Improving efficiency of KD-trees • Implementing R*-trees

  21. Tasks • Surface flattening • Spatial indexing • Format conversion • Code cleanup

  22. Binary STL ASCII STL Facet format Format conversion • Converting betweenSTL and facet format • Loading and saving these formats • Collapsing close points

  23. d Close points Problem: Identify and collapse all pairs of closely located points. • Methods: • R-tree indexing, RTree • Grid indexing, GridSearchTree

  24. Grid indexing Indexing of points by their locationsin a grid of equal-size cubes.

  25. ; the edge length is twice largerthan the collapsing distance d • Index points by cube locations; each cube is a bucket of points 2 · d • Given a point, retrieve the points in the same cube and seven adjacent cubes Grid indexing • Divide the space into cubes

  26. Exactcomparisons Performance 1,000 100 R-treeindexing Grid indexing 10 seconds 1 0.1 0.01 10,000 100 1,000 10 number of facets Grid loading is ten to hundred times faster than R-tree loading.

  27. z y x z y x Future extensions • Grid with templates for general use in CUBIT • Basic repair of surfaces

  28. Tasks • Surface flattening • Spatial indexing • Format conversion • Code cleanup

  29. Code cleanup • User commands for saving faceted surfaces in STL and facet format • Newton-Raphson procedure in the advancing-front meshing • Arguments and returned values in the procedures for cutting spatial objects • Testing the beta version of CUBIT 8

  30. z y x • Decimation of facets z y • Repair of surfaces x z y • Smooth representation x z y x Future tasks • Topology extraction

  31. Matt Boonstra Plamen Stoyanov Kevin Albrecht New student New student Future tasks • Decimation of facets • Repair of surfaces • Smooth representation • Topology extraction

More Related