1. 1 On-the-Fly Transformation and Rendering of Compressed Irregular Volume Data Chuan-kai Yang Department of Computer Science State University of New York at Stony Brook

2. 2 Volume Rendering Raycasting: direct volume rendering:

3. 3 Raycasting Opacity, Color f

4. 4 IrregularRegular Regular/Irregular Grids Cartesian gridsrectilinear gridscurvilinear grids unstructured/tetrahedral grids hybrid grids

5. 5 Huge Data Sets Technological advances in data acquisition devices Data irregularity  Huge data sets  data sets are stored in compressed format How to render a compressed data set?

6. 6 Strategies Decompression before rendering Latency, data loading time, memory requirement On-the-fly decompression during rendering On-the-fly rendering during decompression Rendering in the compression domain

7. 7 What’s Next? Visualization is too slow!  Volume Simplification!

8. 8 Surface Simplification

9. 9 Volume Simplification original 80% simplified

10. 10 Volume Simplification – Contd. original80% simplified95% simplified original80% simp., metric 180% simp., metric 2

11. 11 Outline Related work One-the-fly rendering of compressed irregular grids – Gatun1 On-the-fly simplification and rendering of compressed irregular grids – Gatun2 Time-critical rendering Conclusion Future/past work

12. 12 Outline On-the-fly rendering of compressed irregular grids On-the-fly simplification and rendering of compressed irregular grids Time-critical rendering Conclusion Future/past work

13. 13 On-the-Fly Rendering of Compressed Irregular Grids Tetrahedron compression Garrity-Hong-Bunyk’s rendering Gatun’s rendering Performance results

14. 14 Tetrahedron Compression – 1 Represent the “fourth vertex” implicitly: Edge-adjacent face cur fourth vertex

15. 15 Tetrahedron Compression – 2 Vertex-adjacent face cur fourth vertex New Vertex Index

16. 16 Garrity-Hong-Bunyk’s Algo.

17. 17 G-H-B’s Algorithm – Contd.

18. 18 Gatun’s Inward Compression

19. 19 Gatun’s Rendering G-H-B’s algorithm is already very fast, so we try to reduce the memory footprint… Principle 1: once a decompressed tetrahedron is rendered, it can be thrown away (Garbage collection!) Principle 2: a decompressed tetrahedron should be rendered as soon as possible

20. 20 Decompression Order Tetrahedron decompression order may not be favored by the renderer a b A B E Decompressed order: A, B, C, D, E C D Check face projections Vertex projection Classification

21. 21 Classification Using at most four cross-products, the projection of a given tetrahedron can be classified

22. 22 Readiness Check A tetrahedron becomes “ready” only if the projection of its processed faces can cover its projection Once a tetrahedron is rendered, all of its “unprocessed” faces become “processed” Boundary faces first become “processed”

23. 23 Decomp. Order, Revisited What if D, E are decompressed first? A B E C D

24. 24 Bi-directional Rendering Data set segment sub-seg high watermark low watermark

25. 25 Performance Results Liquid Oxygen PostDelta Wing

26. 26 Performance Results – Contd. Six data sets: 180K to 1M tetrahedra Out-of-core: one or two order of magnitudes better In-core: 30% improvement Peak memory Saving: 50% to 70%

27. 27 Outline On-the-fly rendering of compressed irregular grids On-the-fly simplification and rendering of compressed irregular grids Time-critical rendering Conclusion Future/past work

28. 28 On-the-Fly Simp. and Rend. of Compressed Irregular Grids Static volume simplification Run-time volume simplification On-the-fly simplification and rendering of compressed irregular grids Performance results

29. 29 Static Volume Simplification Gelder ’99 11 6 8 7 4 9 8 7 4 6 9 8 7 4 6 9 8 7 4 6 9 8 7 4 6 9 8 7 4 6 9

30. 30 Static Volume Simp. – Contd. Priority queue is used to build the simplification hierarchy Each “vertex merge” is associated with a “rank” Build the merge-trees structure

31. 31 Run-time Volume Simplification 8 6213 3104111 57 1412 915 1 7 9 5 4 8 10 13 6 212 14 3 11 Merge Tree 15 vertices 14 vertex merges Example: 7 vertex merges

32. 32 Decomp. & Simp. & Rendering A B C D E F A B C D E F 1 Rendering G 2 Vertex C:2 3 3 Discard 2 4 Rendering GCGC

33. 33 Performance Results Generic Gatun2 Simplification overhead: less then 5% of total execution time

34. 34 Outline On-the-fly rendering of compressed irregular grids On-the-fly simplification and rendering of compressed irregular grids Time-critical rendering Conclusion Future/past work

35. 35 Time-critical Rendering What’s missing? Determine the simplification ratio Fixed frame rate Decompression overhead More than 50% of total execution time at simplification ratio 0.9

36. 36 Multi-resolution Pre-simp. Pre-simplified configurations Simplification ratio= 1 – 2 -i, i= 0, 1, 2, … Compression overhead is fixed 0 10.50.750.875 …

37. 37 Outline On-the-fly rendering of compressed irregular grids On-the-fly simplification and rendering of compressed irregular grids Time-critical rendering Conclusion Future/past work

38. 38 Conclusion A powerful scheme of combining lossless and lossy volume compressions in one framework <2.5 bits/tetra RMSE<0.12 (range: 0 – 255) at simp. ratio 0.9 Capable of time-critical rendering

39. 39 Outline On-the-fly rendering of compressed irregular grids On-the-fly simplification and rendering of compressed irregular grids Time-critical rendering Conclusion Future/past work

40. 40 Future Work Integration of compression, (view- independent of view-dependent) simplification and rendering for surface or volumetric meshes Layered representation for out-of-core iso-surface extraction

41. 41 Layered Representation Value-based decomposition v.s. Space-based decomposition

42. 42 Algorithm Sub-range (layer) calculation Histogram: each sub-range should capture roughly the same number of tetrahedra Distribution: each tetrahedron is sent to the sub-range that intersects it Binary search to locate a sub-range for a given iso-value query

43. 43 Discussion Always a roughly fixed proportion is touched More friendly to triangle strips generation Each sub-mesh may be compressed On-the-fly iso-surface extraction of compressed irregular grids

44. 44 Compression Domain Rendering of Regular Grids Whole volume FPST Pros and cons Block-based FPST Pros and cons

45. 45 Whole Volume FPST slice 2D Fourier Transform Image plane Spatial domain 3D Fourier Transform Frequency domain projection (X-ray like images) Dunne et al. 90 and Malzbender et al. 93 

46. 46 Pros and Cons Asymptotically faster  Aliasing and ghost effects along boundary  Xray-like, lack of self-occlusion

47. 47 Block-Based FPST 2D 3D Chiueh et al. 97

48. 48 Pros and Cons Approximated self-occlusion  Aliasing and ghost effects along block boundary  Approximation with average  Overlapped partition or not?

49. 49 Out-of-Core, I/O Conscious Volume Rendering Masking I/O by Computation! How to load blocks in a correct order? Image plane 1 2 3 4 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 y x

50. 50 Automatic Application-Specific File Prefetching System Source-to-Source I/O related code extraction Modify Kernel to schedule the prefetch thread far ahead Automatic I/O prefetching!

51. 51 Zodiac: A Video Authoring System

52. 52 Further Optimizations Wasted effort on small tetrahedrons Pre-filtering Non-filtered and no-contribution rate: < 10% Early-ray termination Pseudo early-ray termination Single-segment rate: > 90% AB C D