1 / 58

Scientific Visualization

Melanie Tory Acknowledgments: Torsten M öller (Simon Fraser University) Raghu Machiraju (Ohio State University) Klaus Mueller (SUNY Stony Brook). Scientific Visualization. Overview. What is SciVis? Data & Applications Iso-surfaces Direct Volume Rendering Vector Visualization Challenges.

hammett-livingston
Download Presentation

Scientific Visualization

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. Melanie Tory Acknowledgments: Torsten Möller (Simon Fraser University) Raghu Machiraju (Ohio State University)Klaus Mueller (SUNY Stony Brook) Scientific Visualization

  2. Overview • What is SciVis? • Data & Applications • Iso-surfaces • Direct Volume Rendering • Vector Visualization • Challenges

  3. SciVis InfoVis Difference between SciVis and InfoVis Parallel Coordinates Direct Volume Rendering [Hauser et al.,Vis 2000] [Fua et al., Vis 1999] Isosurfaces Glyphs Scatter Plots Line Integral Convolution [http://www.axon.com/gn_Acuity.html] Node-link Diagrams [Cabral & Leedom,SIGGRAPH 1993] Streamlines [Lamping et al., CHI 1995] [Verma et al.,Vis 2000]

  4. Difference between SciVis and InfoVis • Card, Mackinlay, & Shneiderman: • SciVis: Scientific, physically based • InfoVis: Abstract • Munzner: • SciVis: Spatial layout given • InfoVis: Spatial layout chosen • Tory & Möller: • SciVis: Spatial layout given + Continuous • InfoVis: Spatial layout chosen + Discrete • Everything else -- ?

  5. Overview • What is SciVis? • Data & Applications • Iso-surfaces • Direct Volume Rendering • Vector Visualization • Challenges

  6. Medical Scanning • MRI, CT, SPECT, PET, ultrasound

  7. Medical Scanning - Applications • Medical education for anatomy, surgery, etc. • Illustration of medical procedures to the patient

  8. Medical Scanning - Applications • Surgical simulation for treatment planning • Tele-medicine • Inter-operative visualization in brain surgery, biopsies, etc.

  9. Biological Scanning • Scanners: Biological scanners, electronic microscopes, confocal microscopes • Apps – physiology, paleontology, microscopic analysis…

  10. Industrial Scanning • Planning (e.g., log scanning) • Quality control • Security (e.g. airport scanners)

  11. Scientific Computation - Domain • Mathematical analysis • ODE/PDE (ordinary and partialdifferential equations) • Finite element analysis (FE) • Supercomputer simulations

  12. Scientific Computation - Apps • Flow Visualization

  13. Overview • What is SciVis? • Data & Applications • Iso-surfaces • Direct Volume Rendering • Vector Visualization • Challenges

  14. Isosurfaces - Examples Isolines Isosurfaces

  15. Isosurface Extraction 0 1 1 3 2 • by contouring • closed contours • continuous • determined by iso-value • several methods • marching cubes is most common 1 3 6 6 3 3 7 9 7 3 2 7 8 6 2 1 2 3 4 3 Iso-value = 5

  16. MC 1: Create a Cube • Consider a Cube defined by eight data values: (i,j+1,k+1) (i+1,j+1,k+1) (i,j,k+1) (i+1,j,k+1) (i,j+1,k) (i+1,j+1,k) (i,j,k) (i+1,j,k)

  17. MC 2: Classify Each Voxel • Classify each voxel according to whether it liesoutside the surface (value > iso-surface value)inside the surface (value <= iso-surface value) 10 10 Iso=9 5 5 10 8 Iso=7 8 8 =inside =outside

  18. MC 3: Build An Index • Use the binary labeling of each voxel to create an index v8 v7 11110100 inside =1 v4 outside=0 v3 v5 v6 00110000 v1 v2 Index: v3 v5 v6 v1 v2 v4 v7 v8

  19. MC 4: Lookup Edge List • For a given index, access an array storing a list of edges • all 256 cases can be derived from 15 base cases

  20. MC 4: Example • Index = 00000001 • triangle 1 = a, b, c c a b

  21. MC 5: Interp. Triangle Vertex • For each triangle edge, find the vertex location along the edge using linear interpolation of the voxel values i+1 i x =10 =0 T=8 T=5

  22. MC 6: Compute Normals • Calculate the normal at each cube vertex • Use linear interpolation to compute the polygon vertex normal

  23. MC 7: Render!

  24. Overview • What is SciVis? • Data & Applications • Iso-surfaces • Direct Volume Rendering • Vector Visualization • Challenges

  25. Direct Volume Rendering Examples

  26. Rendering Pipeline (RP) Classify

  27. Classification • original data set has application specific values (temperature, velocity, proton density, etc.) • assign these to color/opacity values to make sense of data • achieved through transfer functions

  28. a(f) RGB(f) Shading, Compositing… Human Tooth CT Transfer Functions (TF’s) RGB a • Simple (usual) case: Map data value f to color and opacity f Gordon Kindlmann

  29. TF’s • Setting transfer functions is difficult, unintuitive, and slow a a f f a a f f Gordon Kindlmann

  30. Transfer Function Challenges • Better interfaces: • Make space of TFs less confusing • Remove excess “flexibility” • Provide guidance • Automatic / semi-automatic transfer function generation • Typically highlight boundaries Gordon Kindlmann

  31. Rendering Pipeline (RP) Classify Shade

  32. Light Effects • Usually only considering reflected part Light reflected specular Light absorbed ambient diffuse transmitted Light=refl.+absorbed+trans. Light=ambient+diffuse+specular

  33. Rendering Pipeline (RP) Classify Shade Interpolate

  34. 1D • Given: • Needed: • Needed: Interpolation 2D • Given:

  35. Interpolation • Very important; regardless of algorithm • Expensive => done very often for one image • Requirements for good reconstruction • performance • stability of the numerical algorithm • accuracy Linear Nearest neighbor

  36. Rendering Pipeline (RP) Classify Shade Interpolate Composite

  37. Ray Traversal Schemes Intensity Max Average Accumulate First Depth

  38. Ray Traversal - First Intensity • First: extracts iso-surfaces (again!)done by Tuy&Tuy ’84 First Depth

  39. Ray Traversal - Average Intensity • Average: produces basically an X-ray picture Average Depth

  40. Ray Traversal - MIP Intensity • Max: Maximum Intensity Projectionused for Magnetic Resonance Angiogram Max Depth

  41. Ray Traversal - Accumulate Intensity • Accumulate: make transparent layers visible!Levoy ‘88 Accumulate Depth

  42. 1.0 Volumetric Ray Integration color opacity object (color, opacity)

  43. Overview • What is SciVis? • Data & Applications • Iso-surfaces • Direct Volume Rendering • Vector Visualization • Challenges

  44. Flow Visualization • Traditionally – Experimental Flow Vis • Now – Computational Simulation • Typical Applications: • Study physics of fluid flow • Design aerodynamic objects

  45. Traditional Flow Experiments

  46. Glyphs (arrows) Techniques Contours Jean M. Favre Streamlines

  47. Techniques

  48. Techniques - Stream-ribbon • Trace one streamline and a constant size vector with it • Allows you to see places where flow twists

  49. Techniques - Stream-tube • Generate a stream-line and widen it to a tube • Width can encode another variable

  50. Mappings - Flow Volumes • Instead of tracing a line - trace a small polyhedron

More Related