2. 1 Scientific Visualization Melanie Tory Acknowledgments: Torsten Möller (Simon Fraser University) Raghu Machiraju (Ohio State University) Klaus Mueller (SUNY Stony Brook)

3. 2 Overview 4 What is SciVis? 4 Data & Applications 4 Iso-surfaces 4 Direct Volume Rendering 4 Vector Visualization 4 Challenges

4. 3 Difference between SciVis and InfoVis Direct Volume Rendering Streamlines Line Integral Convolution Glyphs Isosurfaces SciVis Scatter Plots Parallel Coordinates Node-link Diagrams InfoVis [Verma et al., Vis 2000] [Hauser et al., Vis 2000] [Cabral & Leedom, SIGGRAPH 1993] [Fua et al., Vis 1999] [http://www.axon.com/ gn_Acuity.html] [Lamping et al., CHI 1995]

5. 4 Difference between SciVis and InfoVis 4 Card, Mackinlay, & Shneiderman: –SciVis: Scientific, physically based –InfoVis: Abstract 4 Munzner: –SciVis: Spatial layout given –InfoVis: Spatial layout chosen 4 Tory & Möller: –SciVis: Spatial layout given + Continuous –InfoVis: Spatial layout chosen + Discrete –Everything else -- ?

6. 5 Overview 4 What is SciVis? 4 Data & Applications 4 Iso-surfaces 4 Direct Volume Rendering 4 Vector Visualization 4 Challenges

7. 6 Medical Scanning 4 MRI, CT, SPECT, PET, ultrasound

8. 7 Medical Scanning - Applications 4 Medical education for anatomy, surgery, etc. 4 Illustration of medical procedures to the patient

9. 8 Medical Scanning - Applications 4 Surgical simulation for treatment planning 4 Tele-medicine 4 Inter-operative visualization in brain surgery, biopsies, etc.

10. 9 Biological Scanning 4 Scanners: Biological scanners, electronic microscopes, confocal microscopes 4 Apps – physiology, paleontology, microscopic analysis…

11. 10 Industrial Scanning 4 Planning (e.g., log scanning) 4 Quality control 4 Security (e.g. airport scanners)

12. 11 Scientific Computation - Domain 4 Mathematical analysis 4 ODE/PDE (ordinary and partial differential equations) 4 Finite element analysis (FE) 4 Supercomputer simulations

13. 12 Scientific Computation - Apps 4 Flow Visualization

14. 13 Overview 4 What is SciVis? 4 Data & Applications 4 Iso-surfaces 4 Direct Volume Rendering 4 Vector Visualization 4 Challenges

15. 14 Isosurfaces - Examples IsolinesIsosurfaces

16. 15 Isosurface Extraction 4 by contouring –closed contours –continuous –determined by iso-value 4 several methods –marching cubes is most common 1234 3 2786 2 3797 3 1366 3 0113 2 Iso-value = 5

17. 16 MC 1: Create a Cube 4 Consider a Cube defined by eight data values: (i,j,k)(i+1,j,k) (i,j+1,k) (i,j,k+1) (i,j+1,k+1)(i+1,j+1,k+1) (i+1,j+1,k) (i+1,j,k+1)

18. 17 MC 2: Classify Each Voxel 4 Classify each voxel according to whether it lies outside the surface (value > iso-surface value) inside the surface (value <= iso-surface value) 8 Iso=7 8 8 5 5 10 Iso=9 =inside =outside

19. 18 MC 3: Build An Index 4 Use the binary labeling of each voxel to create an index v1v2 v6 v3 v4 v7 v8 v5 inside =1 outside=0 11110100 00110000 Index: v1v2 v3 v4 v5v6 v7v8

20. 19 MC 4: Lookup Edge List 4 For a given index, access an array storing a list of edges 4 all 256 cases can be derived from 15 base cases

21. 20 MC 4: Example 4 Index = 00000001 4 triangle 1 = a, b, c a b c

22. 21 MC 5: Interp. Triangle Vertex 4 For each triangle edge, find the vertex location along the edge using linear interpolation of the voxel values =10 =0 T=8 T=5 i i+1 x

23. 22 MC 6: Compute Normals 4 Calculate the normal at each cube vertex 4 Use linear interpolation to compute the polygon vertex normal

24. 23 MC 7: Render!

25. 24 Overview 4 What is SciVis? 4 Data & Applications 4 Iso-surfaces 4 Direct Volume Rendering 4 Vector Visualization 4 Challenges

26. 25 Direct Volume Rendering Examples

27. 26 Rendering Pipeline (RP) Classify

28. 27 Classification 4 original data set has application specific values (temperature, velocity, proton density, etc.) 4 assign these to color/opacity values to make sense of data 4 achieved through transfer functions

29. 28 Transfer Functions (TF’s) 4 Simple (usual) case: Map data value f to color and opacity Human Tooth CT (f)(f) RGB(f)RGB(f) f RGBRGB Shading, Compositing…  Gordon Kindlmann

30. 29 TF’s 4 Setting transfer functions is difficult, unintuitive, and slow f  f  f  f  Gordon Kindlmann

31. 30 Transfer Function Challenges 4 Better interfaces: –Make space of TFs less confusing –Remove excess “flexibility” –Provide guidance 4 Automatic / semi-automatic transfer function generation –Typically highlight boundaries Gordon Kindlmann

32. 31 Rendering Pipeline (RP) Classify Shade

33. 32 Light Effects 4 Usually only considering reflected part Light absorbed transmitted reflected Light=refl.+absorbed+trans. Light ambient specular diffuse Light=ambient+diffuse+specular

34. 33 Rendering Pipeline (RP) Classify Shade Interpolate

35. 34 Interpolation 4 Given: 4 Needed: 2D 1D 4 Given: 4 Needed:

36. 35 Interpolation 4 Very important; regardless of algorithm 4 Expensive => done very often for one image 4 Requirements for good reconstruction –performance –stability of the numerical algorithm –accuracy Nearest neighbor Linear

37. 36 Rendering Pipeline (RP) Classify Shade Interpolate Composite

38. 37 Ray Traversal Schemes Depth Intensity Max Average Accumulate First

39. 38 Ray Traversal - First Depth Intensity First 4 First: extracts iso-surfaces (again!) done by Tuy&Tuy ’84

40. 39 Ray Traversal - Average Depth Intensity Average 4 Average: produces basically an X-ray picture

41. 40 Ray Traversal - MIP Depth Intensity Max 4 Max: Maximum Intensity Projection used for Magnetic Resonance Angiogram

42. 41 Ray Traversal - Accumulate Depth Intensity Accumulate 4 Accumulate: make transparent layers visible! Levoy ‘88

43. 42 Volumetric Ray Integration color opacity object (color, opacity) 1.0

44. 43 Overview 4 What is SciVis? 4 Data & Applications 4 Iso-surfaces 4 Direct Volume Rendering 4 Vector Visualization 4 Challenges

45. 44 Flow Visualization 4 Traditionally – Experimental Flow Vis 4 Now – Computational Simulation 4 Typical Applications: –Study physics of fluid flow –Design aerodynamic objects

46. 45 Traditional Flow Experiments

47. 46 Techniques Contours Streamlines Glyphs (arrows) Jean M. Favre

48. 47 Techniques

49. 48 Techniques - Stream-ribbon 4 Trace one streamline and a constant size vector with it 4 Allows you to see places where flow twists

50. 49 Techniques - Stream-tube 4 Generate a stream-line and widen it to a tube 4 Width can encode another variable

51. 50 Mappings - Flow Volumes 4 Instead of tracing a line - trace a small polyhedron

52. 51 LIC (Line Integral Convolution) 4 Integrate noise texture along a streamline H.W. Shen

53. 52 Overview 4 What is SciVis? 4 Data & Applications 4 Iso-surfaces 4 Direct Volume Rendering 4 Vector Visualization 4 Challenges

54. 53 Challenges - Accuracy 4 Need metrics -> perceptual metric

55. 54 Challenges - Accuracy 4 Deal with unreliable data (noise, Ultrasound)

56. 55 Challenges - Accuracy 4 Irregular data sets regularrectilinearuniformcurvilinear Structured Grids: regularirregularhybridcurved Unstructured Grids:

57. 56 Challenges - Speed/Size 4 Efficient algorithms 4 Hardware developments (VolumePro) 4 Utilize current hardware (nVidia, ATI) 4 Compression schemes 4 Tera-byte data sets

58. 57 Challenges - HCI 4 Need better interfaces 4 Which method is best?

59. 58 Challenges - HCI 4 “Augmented” reality 4 Explore novel I/O devices