1. UVA / UNC / JHU Perceptually Guided Simplification of Lit, Textured Meshes Nathaniel WilliamsUNC David LuebkeUVA Jonathan D. CohenJHU Michael KelleyUVA Brenden SchubertUVA

2. UVA / UNC / JHU Motivation: large datasets Scanning Monticello Project In 10 hours we collected 185,000,000 point samples with a scanning laser rangefinder

3. UVA / UNC / JHU Solution: level of detail Simplify complex models to achieve interactivity 25+ years of active research [Clark 1976]

4. UVA / UNC / JHU The key issues How should we simplify the data? How should we regulate the level of detail? How should we evaluate the results?

5. UVA / UNC / JHU Our approach: Perceptually guided simplification Regulate level of detail with a low-level model of human vision Budget-based simplification Unified framework for LOD selection sensitive to ♦ Silhouettes ♦ Texture ♦ Dynamic lighting No parameters to tweak

6. UVA / UNC / JHU Previous work: Perceptually based graphics Human in the loop ♦ User-guided simplification Li & Watson 2001 Kho & Garland 2003 Pojar & Schmalstieg 2003 ♦ Level of detail evaluation Watson et al. 2001 O’Sullivan & Dingliana 2001

7. UVA / UNC / JHU Previous work: Perceptually based graphics Automatic metrics ♦ Global illumination Ramasubramanian et al. 1999 ♦ LOD frequency content Reddy 1996, 2001 ♦ Image-driven simplification Lindstrom & Turk 2000 ♦ Luebke & Hallen 2001 Focus on “imperceptible simplification” Limited to Gouraud-shaded models with per- vertex color

8. UVA / UNC / JHU Perceptual model: The contrast sensitivity function Model is based on contrast gratings Spatial Frequency (cycles/degree) Contrast Courtesy of Izumi Ohzawa

9. UVA / UNC / JHU Perceptual model: The contrast sensitivity function Predicts the threshold perceptibility of a stimulus given its size and contrast Figure courtesy of Martin Reddy

10. UVA / UNC / JHU Perceptual model: The contrast sensitivity function Following Luebke & Hallen 2001, we liken local simplification operations to a worst-case contrast grating We calculate ♦ Maximum Michelson contrast ♦ Minimum spatial frequency

11. UVA / UNC / JHU Maximum Michelson contrast Y min Y max

12. UVA / UNC / JHU Minimum spatial frequency Ф r

13. UVA / UNC / JHU Texture deviation Distance between corresponding 3D points through P mesh M i mesh M i+1 2D texture domain (i+1) st edge collapse XiXiXiXi X i+1 x P

14. UVA / UNC / JHU Texture deviation Improved bound on the size of features altered by simplification

15. UVA / UNC / JHU The Multi-Triangulation Directed acyclic graph ♦ Nodes Edge collapse operations ♦ Arcs Node dependencies Mesh triangles Triangles are explicitly represented ♦ Good for preprocessing

16. UVA / UNC / JHU Preprocessing Augment each Multi-Triangulation node with additional information ♦ Parametric texture deviation ♦ Minimum bounding sphere ♦ Texture luminance Y min and Y max ♦ Normal cone for silhouette test ♦ Normal cone for illumination test

17. UVA / UNC / JHU Run-time simplification Simplification to a triangle budget Dual-queue approach ♦ ROAM [Duchaineau et al. 1997] ♦ Start with cut from previous frame ♦ Exploit temporal coherence Calculate perceptual error of nodes given the current viewing frustum

18. UVA / UNC / JHU Silhouette contrast We determine a node’s silhouette status with the normal cone ♦ Luebke & Erikson 1997 We conservatively assume that silhouette nodes have maximal contrast

19. UVA / UNC / JHU Illumination contrast Diffuse Specular

20. UVA / UNC / JHU Demonstration Show Video

21. UVA / UNC / JHU Evaluation Perceptually motivated image metric ♦ ltdiff [Lindstrom 2000] Comparison to a Multi-Triangulation based implementation of Appearance Preserving Simplification ♦ Cohen et al. 1998

22. UVA / UNC / JHU Results 500,000 triangle armadillo with per-vertex normals

23. UVA / UNC / JHU Results: 98% simplified Screen-space Error: 3,689 Perceptually guided Error: 3,123 Error Low High

24. UVA / UNC / JHU Results: memory usage 500,000 triangle armadillo Memory Original model13.6 MB Multi-Triangulation66.3 MB Perceptually Guided74.9 MB

25. UVA / UNC / JHU Discussion: Pros Unified framework for interactive rendering ♦ Based on perceptual metric (CSF) ♦ Sensitive to texture, illumination, and silhouettes ♦ Parameter-free No tweaking required!

26. UVA / UNC / JHU Discussion: Cons View-dependent LOD is costly ♦ Increased memory requirements ♦ Higher CPU load ♦ Less well suited for current GPUs Summary: high fidelity, automatic simplification…for a price

27. UVA / UNC / JHU Future work Improved perceptual models ♦ Supra-threshold contrast sensitivity ♦ Visual masking using texture content ♦ Eccentricity & velocity MIP-map filtering ♦ Critical for terrain models User studies

28. UVA / UNC / JHU Acknowledgements People ♦ Peter Lindstrom ♦ Martin Reddy Funding ♦ National Science Foundation Images and models: ♦ Stanford 3-D Scanning Repository for the Bunny ♦ Caltech for the Armadillo ♦ Martin Reddy for CSF plot ♦ Campbell-Robson Chart by Izumi Ohzawa

29. UVA / UNC / JHU The End