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Low-Level Visual Abstractions User-Centric Scientific Visualization Stefan Bruckner Illustrative Visualization Definition : computer supported interactive and expressive visualizations of complex data through abstractions from traditional


  1. Low-Level Visual Abstractions User-Centric Scientific Visualization Stefan Bruckner

  2. Illustrative Visualization Definition : computer supported interactive and expressive visualizations of complex data through abstractions from traditional illustrations 2

  3. Low-Level Abstraction Techniques Concerned with how different objects are presented Stylized depiction Silhouettes and contours, pen and ink, stippling, hatching, ... 3

  4. Media and Styles in Illustration 4

  5. Non-Photorealistic Rendering Definition : Computer-generated stylized depiction of the 3D synthetic scene or a real-world 2D capture. [Lum and Ma 2002] 5

  6. Shading Setup 6

  7. Phong Local Illumination Model 7

  8. Toon Shading 8

  9. Shading Textures contour cartoon metal Phong-Blinn enhancement shading shading shading Stefan Bruckner 9

  10. Cool-to-Warm Shading [Gooch et al. 1998] 10

  11. Exaggerated Shading [Rusinkiewicz et al. 2006] 11

  12. Geometry-Dependent Lighting [Lee et al. 2005] 12

  13. Lit Sphere Style Transfer [Sloan et al. 2001] 13

  14. Lit Sphere Concept Use a sphere map indexed by the eye-space normal to determine the color of a point [Sloan et al. 2001] 14

  15. Style Transfer Functions 15

  16. Style Transfer Functions [Bruckner and Groeller 2007] 16

  17. Stylization and Abstraction of Photographs [DeCarlo and Santella2002] 17

  18. Smoothing Filter 1 1 1 1 1 1 1 1 1 Mean Filter 1 2 1 2 4 2 1 2 1 Gaussian Filter 18

  19. Edge-Detectors Filter 1 1 1 1 -8 1 1 1 1 Laplacian Filter -1 0 1 -2 0 2 -1 0 1 Sobel Filter 19

  20. Bilateral Filter [Tomasi and Manduchi 1998] 20

  21. Stylization and Abstraction of Photographs [DeCarlo and Santella2002] 21

  22. Flow-Based Image Abstraction [Kang et al. 2009] 22

  23. Line-Integral Convolution Image Abstraction [Kyprianidis and Kang 2011] 23

  24. Line Drawing 24

  25. Silhouette Extraction in Image Space Generated from the Depth map (Z-Buffer) Detect C0 surface discontinuities [Hertzmann et al. 1999] 25

  26. Inner Contour Extraction in Image Space Generated from the Normal map Detect C1 surface discontinuities [Hertzmann et al. 1999] 26

  27. Contour Composition in Image Space Combination Depth + Normal map [Hertzmann et al. 1999] 27

  28. Contour Extraction in Object Space Gradient ∇ computed by a first-order derivative filter (Central Differences, Sobel) Gradient ∇ approximates normal vector of implicit surface N = - ∇ / │ ∇ │ contour= g( │ ∇ │)( 1-| N . V |) e V = view vector e = exponent determines silhouette range [Ebert and Rheingans 2000] [Csebfalvi et al. 2001] 28

  29. Outline Extraction for Volumes Exponent between 4 and 16 is good choice exponent too low! [Hadwiger et al. 2006] 29

  30. Gradient computation 0 0 0 -1 0 1 0 0 0 Central Differences -1 0 1 -2 0 2 -1 0 1 Sobel Filter 30

  31. Problems of Implicit Surface Contours Constant threshold on Thickness changes! [Hadwiger et al. 2006] 31

  32. Curvature: Second-Order Derivative How do small positional changes on the surface change the normal vector? “derivative” of normal Normal curvature in a direction First and second principal curvature: maximum: minimum: Obtained through eigenanalysis: Characteristic equation Curvature directions Curvature magnitudes [Kindlmann et al. 2003] 32

  33. Curvature Computation Build on consecutive forward-backward differences to obtain n Hessian matrix H Hessian contains curvature information N = - Δ f H = ∇ N T Curvature magnitudes: eigenvalues of 3x3 matrix Curvature directions: eigenvectors of 3x3 matrix [Kindlmann et al. 2003] 33

  34. Hessian Matrix Computation 0 -1 1 0 -2 2 0 -1 1 -1 1 0 -2 2 0 -1 1 0 34

  35. Eigenvalues & -vectors Computation H v = λ v ( H − λ I )v = 0 det ( H − λ I) = 0 35

  36. The Principal Curvature Domain Maximum/minimum principal curvature magnitude Identification of different shapes in 2D domain Elliptical, parabolic, hyperbolic, umbilical points Feature lines: e.g., ridges and valleys [Kindlmann et al. 2003] 36

  37. Curvature Transfer Functions Color coding of curvature domain Paint features: ridge and valley lines Def: locations of max curvature [Kindlmann et al. 2003] 37

  38. Curvature-Based Contour Threshold Higher curvature in view direction needs higher threshold [Kindlmann et al. 2003] 38

  39. Curvature-Based Contour Threshold Threshold dependent on curvature in view direction Thickness constant! [Kindlmann et al. 2003] 39

  40. Curvature-Based Light Warping [Vergne et al. 2009] 40 Ivan Viola

  41. Suggestive Contours [DeCarlo et al. 2003] 41

  42. Suggestive Contour Definition Almost contours: The suggestive contour generator is the set of minima of N.V in the direction of W [DeCarlo et al. 2003] 42

  43. Apparent Ridges Draw lines at rapid variation of normal with respect to the image position Aparent Ridge: loci of points that maximize view dependent curvature [Judd et al. 2007] 43

  44. Aparent Ridges vs Ridges 44

  45. Stippling [Secord 2002] 45

  46. 3D Texture-Based Stippling [Krueger and Westermann 2007] 46

  47. Conveying 3D Shape with Principal Curvature [Interrante et al. 1998] 47

  48. Hatching Contour generation Contour extraction spline generation Set of aligned brush strokes applied to a spline Hatching Distribution function: evenly spaced streamlines Vectorfield is defined by principal curvature directions Stroke length is defined by shading intensity Multi-scale strokes [Gerl 2006] 48

  49. Thanks Slides have been compiled using slides, videos, images, and ideas from: Ivan Viola Tilke Judd Meister Gröller Peter-Pike Sloan Dough DeCarlo Moritz Gerl Adrian Secord Takafumi Saito Kevin Hulsey Anna Vilanova Mario Costa Sousa Jens Krüger Eric Lum Vittoria Interrante Amy and Bruce Gooch Romain Vergne Gordon Kindlmann Henry Kang Markus Hadwiger and many others! Jiri Hladuvka Balazs Csebfalvi 49

  50. References [www.khulsey.com] K. Hulsey, Hand-crafted illustration webpage, www.khulsey.com [Lum and Ma 2002] E. Lum and K.-L. Ma, Hardware-Accelerated Parallel Non-Photorealistic Volume Rendering, NPAR 2002 [Gooch et al. 1998] A. Gooch et al., A Non-Photorealistic Lighting Model for Automatic Technical Illustration, SIGGRAPH 1998 [Rusinkiewicz et al. 2006] S. Rusinkiewicz et al., Exaggerated Shading for Depicting Shape and Detail, SIGGRAPH 2006 [Lee et al. 2005] C. H. Lee et al., Light Collages: Lighting Design for Effective Visualization, IEEE Visualization 2004 [Vergne et al. 2009] R. Vergne et al. Light warping for enhanced surface depiction. ACM SIGGRAPH 2009 [Sloan et al. 2001] P. Sloan et al., The Lit Sphere: A Model for Capturing NPR Shading from Art, Graphics Interface 2001 [Bruckner and Groeller 2007] S. Bruckner and M. E. Groeller, Style Transfer Functions for Illustrative Volume Rendering, Eurographics2007 [Secord 2002] A. Secord, Weighted Voronoi Stippling, NPAR 2002 [Krueger and Westermann 2007] J. Krüger, R. Westermann, Efficient Stipple Rendering, IADIS Computer Graphics and Visualization 2007 [Interrante et al. 1998] V. Interrante et al., Conveying the 3D Shape of Smoothly Curving Transparent Surfaces via Texture, IEEE TVCG 1998 [Gerl 2006] M. Gerl, Volume Hatching for Illustrative Visualization, Master Thesis 2006 [DeCarlo and Santella2002] D. DeCarlo and A. Santella, Stylization and Abstraction of Photographs, SIGGRAPH 2002 [Tomasi and Manduchi 1998] C. Tomasi and R. Manduchi, Bilateral Filtering for Gray and Color Images, IEEE ICCV 1998 [Winnemoeller et al. 2006] H. Winnemoeller et al., Real-time Video Abstraction, SIGGRAPH 2006 [Kang et al. 2009] H. Kang et al. Flow-Based Image Abstraction, IEEE TVCG 2009 [Kyprianidis and Kang 2011] J. Kyprianidis and H.Kang, Image and Video Abstraction by Coherence-Enhancing Filtering, Eurographics 2011 [Hertzmann et al. 1999] A. Hertzmann et al., Introduction to 3D Non-Photorealistic Rendering: Silhouettes and Outlines, Course on Non- Photorealistic Rendering, SIGGRAPH 1999 [Csebfalvi et al. 2001] B. Csebfalvi et al., Fast Visualization of Object Contours by Non-Photorealistic Volume Rendering, Eurographics 2001 [Ebert and Rheingans 2000] David Ebert Penny Rheingans, Volume Illustration: Non-Photorealistic Rendering of Volume Models, IEEE Visualization 2000 [Hadwiger et al. 2006] Non-Photorealistic and Illustrative Techniques, Real-Time Volume Graphics Tutorial (www.real-time-volume- graphics.org), Eurographics2006 [Kindlmann et al 2003] G. Kindlmann et al., Curvature-Based Transfer Functions for Direct Volume Rendering: Methods and Applications, IEEE Visualization 2003 [DeCarlo et al. 2003] D. DeCarlo et al., Suggestive Contours for Conveying Shape, SIGGRAPH 2003 [Judd et al. 2007] T. Judd et al., Apparent Ridges for Line Drawings, SIGGRAPH 2007 50

  51. Books on Non-Photorealistic Rendering 51

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