1. Feature-Based Textures Ganesh Ramanarayanan Kavita Bala Bruce Walter Cornell University EGSR 2004

2. Ganesh Ramanarayanan, Cornell University Motivation Textures have fixed resolution Goal: resolution independent texturing –Improving texture quality when zooming in Applications: games, interactive walkthroughs

3. Ganesh Ramanarayanan, Cornell University Solution Feature-Based Textures (FBTs) –Humans are sensitive to features: boundaries in the texture with sharp contrast –Store features as resolution-independent lines / curves –Interpolate from reachable samples: those on the same side of all features

4. Ganesh Ramanarayanan, Cornell University Quality Using standard texture map

5. Ganesh Ramanarayanan, Cornell University Quality Using Feature-Based Texture

6. Ganesh Ramanarayanan, Cornell University Related Work Vector formats: SVG, Postscript Procedural textures: [Ebert94] Superresolution: [Huang84], [Elad97], [Borman98] Discontinuity detection and use: –Vision: [Canny87], [Perona90], [Malik01] –Point data sets: [Pauly03] –Visibility events: [Drettakis94], [Durand99], [Duguet02] –Discontinuity meshing: [Heckbert92], [Lischinski92] –Silhouette clipping: [Sander00]

7. Ganesh Ramanarayanan, Cornell University Related Work Discontinuity-based 2D representations –NPR: [Salisbury96] –Edge-and-point interactive rendering: [Bala03] –Silhouette shadow maps: [Sen03] Concurrent research –Bixels: [Tumblin04] –Silmap textures: [Sen04]

8. Ganesh Ramanarayanan, Cornell University Outline of Talk Overview FBT Usage FBT Representation Results and Discussion

9. Ganesh Ramanarayanan, Cornell University Overview Each FBT texel stores samples and features One sample in each texel region Most of the FBT works like a standard texture map Only do more computation near features + feature samples

10. Ganesh Ramanarayanan, Cornell University Algorithm Image plane point

11. Ganesh Ramanarayanan, Cornell University Algorithm 1. Map 3D point to 2D texture point p Image plane texel grid point

12. Ganesh Ramanarayanan, Cornell University Algorithm 1. Map 3D point to 2D texture point p 2. Find the texel containing p Image plane texel grid point FBT texel p

13. Ganesh Ramanarayanan, Cornell University Algorithm 1. Map 3D point to 2D texture point p 2. Find the texel containing p 3. Find the texel region containing p FBT texel p Image plane texel grid point

14. Ganesh Ramanarayanan, Cornell University Algorithm 1. Map 3D point to 2D texture point p 2. Find the texel containing p 3. Find the texel region R containing p 4. Look up reachable samples FBT texel p p Image plane texel grid point

15. Ganesh Ramanarayanan, Cornell University Algorithm 1. Map 3D point to 2D texture point p 2. Find the texel containing p 3. Find the texel region R containing p 4. Look up reachable samples 5. Bilinearly interpolate and return result FBT texel p p Image plane texel grid point

16. Ganesh Ramanarayanan, Cornell University Outline of Talk Overview FBT Usage FBT Representation Results and Discussion

17. Ganesh Ramanarayanan, Cornell University Step 3: Region Finding How do we find which of these four regions a point lies in?

18. Ganesh Ramanarayanan, Cornell University Step 3: Region Finding Simple feature: divides texel into two regions Distinguish these regions with a single ray intersection parity test against feature –Cast towards side with no feature intersection 0 1

19. Ganesh Ramanarayanan, Cornell University Step 3: Region Testing n simple features divide area into n+1 regions Identify region using linear search

20. Ganesh Ramanarayanan, Cornell University Step 3: Region Testing n simple features divide area into n+1 regions Identify region using linear search

21. Ganesh Ramanarayanan, Cornell University Step 3: Region Testing n simple features divide area into n+1 regions Identify region using linear search

22. Ganesh Ramanarayanan, Cornell University Step 3: Region Testing n simple features divide area into n+1 regions Identify region using linear search

23. Ganesh Ramanarayanan, Cornell University Feature Intersections When features intersect, parity test for region determination can be ambiguous Solve by introducing horizontal bands

24. Ganesh Ramanarayanan, Cornell University Step 4: Sample Lookup If a texel has no features, use ordinary bilinear interpolation What do we do when there are features? p s 1 2 3 p 13 s2

25. Ganesh Ramanarayanan, Cornell University Step 4: Sample Lookup Sample in lower left region of texel: representative sample Interpolate using region’s sample s and reachable representatives p s 1 2 3 p 11 ss

26. Ganesh Ramanarayanan, Cornell University Step 4: Sample Lookup Sample in lower left region of texel: representative sample Interpolate using region’s sample s and reachable representatives p s 1 2 3 p 33 s3

27. Ganesh Ramanarayanan, Cornell University Outline of Talk Overview FBT Usage FBT Representation Results and Discussion

28. Ganesh Ramanarayanan, Cornell University Creating FBTs Input: samples, features, and FBT resolution Finding features: –Automatic extraction (vectors, feature detection, tracing) –Manual specification (from scratch or extracted features) Vectors Tracing User-drawn

29. Ganesh Ramanarayanan, Cornell University Resolution Tradeoff More FBT texels means: –More memory usage –More texels with no features for faster lookup –Denser sampling of input for better gradients

30. Ganesh Ramanarayanan, Cornell University Samples close to a feature are prefiltered –Such samples should be eliminated Sample Invalidation

31. Ganesh Ramanarayanan, Cornell University Reachability Graph Decompose space into sub-regions

32. Ganesh Ramanarayanan, Cornell University Reachability Graph Decompose space into sub-regions –Divide texel at all feature/feature intersections, feature/texel intersections, curve maxima/minima

33. Ganesh Ramanarayanan, Cornell University Reachability Graph Decompose space into sub-regions –Divide texel at all feature/feature intersections, feature/texel intersections, spline maxima/minima Use sub-regions to form reachability graph

34. Ganesh Ramanarayanan, Cornell University Hole Filling Fill holes with closest available samples

35. Ganesh Ramanarayanan, Cornell University Hole Filling Fill holes with closest available samples Collapse sub-regions into main regions

36. Ganesh Ramanarayanan, Cornell University Outline of Talk Overview FBT Usage FBT Representation Results and Discussion

37. Ganesh Ramanarayanan, Cornell University Example Inputs Stop sign (vector) Yin yang (vector)

38. Ganesh Ramanarayanan, Cornell University Example Inputs Stained glass (user) Flower (user) Wizard skin (user)

39. Ganesh Ramanarayanan, Cornell University Results SVG Vector Format FBT 230x256 416 KB

40. Ganesh Ramanarayanan, Cornell University Results FBT 230x256 416 KB Bilinear 460x512 690 KB FBT 16x16 9 KB Bilinear 64x64 12 KB

41. Ganesh Ramanarayanan, Cornell University Results FBT 128x128 96 KB Bilinear 128x128 48 KB FBT 256x256 434 KB Bilinear 256x256 192 KB

42. Ganesh Ramanarayanan, Cornell University Results – 3D Models FBT 230x256 416 KB Bilinear 460x512 690 KB

43. Ganesh Ramanarayanan, Cornell University Results – 3D Models FBT 230x256 416 KB Bilinear 460x512 690 KB

44. Ganesh Ramanarayanan, Cornell University Results – 3D Models FBT 230x256 416 KB Bilinear 460x512 690 KB

45. Ganesh Ramanarayanan, Cornell University Results – 3D Models FBT 256x256 357 KB Bilinear 256x256 192 KB Artwork from Warcraft® III: Reign of Chaos™ provided courtesy of Blizzard Entertainment

46. Ganesh Ramanarayanan, Cornell University Results – 3D Models Artwork from Warcraft® III: Reign of Chaos™ provided courtesy of Blizzard Entertainment FBT 256x256 357 KB Bilinear 256x256 192 KB

47. Ganesh Ramanarayanan, Cornell University Results – 3D Models Artwork from Warcraft® III: Reign of Chaos™ provided courtesy of Blizzard Entertainment FBT 256x256 357 KB Bilinear 256x256 192 KB

48. Ganesh Ramanarayanan, Cornell University FBT Properties 90% of texels: No features –Most texels similar to standard texture maps –Low amortized lookup cost –Low amortized storage cost 99% of texels: < 2 features –Fixed-size representation possible for GPUs Promising results on NV40 even without using new functionalities (early-out, etc)

49. Ganesh Ramanarayanan, Cornell University Conclusions FBTs combine samples with resolution- independent features Flexible representation encompassing vector and raster image formats Applications: games, interactive walkthroughs Future work: –MIP-mapping and antialiasing –Handling a wider variety of textures

50. Ganesh Ramanarayanan, Cornell University Questions graman@cs.cornell.edu kb@cs.cornell.edu bjw@graphics.cornell.edu FBT Webpage: –http://www.cs.cornell.edu/~graman/papers/egsr04fbt