1. Volume Graphics What’s in the cards…

2. The Panelists Kwan-Liu Ma

3. The Panelists Kwan-Liu MaMin Chen

4. The Panelists Kwan-Liu MaMin ChenBaoquan Chen

5. The Panelists Kwan-Liu MaMin ChenBaoquan ChenMichael Meissner

6. The Panelists Kwan-Liu MaMin ChenBaoquan ChenMichael Meissner Klaus Mueller

7. The Good Cards wide acceptance

8. The Good Cards wide acceptance available data

9. The Good Cards wide acceptance available data lots of research

10. The Good Cards wide acceptance available data lots of research speed (GPU)

11. Ten Issues at VG99 Storage (polys vs. voxels) Effects (reflections, refractions, shadows) Radiosity – is it easier/better with voxels? Potential for modeling Can the “Visible Human” walk? Volume test data (a teapot with actual tea) Role of image processing and computer vision A stack of images is a volume (modeling) Availability of real-time volume rendering Penetration of volume graphics into other disciplines (think Siggraph…)

12. Ten Issues at VG99 Storage (polys vs. voxels) Effects (reflections, refractions, shadows) Radiosity – is it easier/better with voxels? Potential for modeling Can the “Visible Human” walk? Volume test data (a teapot with actual tea) Role of image processing and computer vision A stack of images is a volume (modeling) Availability of real-time volume rendering Penetration of volume graphics into other disciplines (think Siggraph…)

13. Ten Issues at VG99 Storage (polys vs. voxels) Effects (reflections, refractions, shadows) Radiosity – is it easier/better with voxels? Potential for modeling Can the “Visible Human” walk? Volume test data (a teapot with actual tea) Role of image processing and computer vision A stack of images is a volume (modeling) Availability of real-time volume rendering Penetration of volume graphics into other disciplines (think Siggraph…)

14. Ten Issues at VG99 Storage (polys vs. voxels) Effects (reflections, refractions, shadows) Radiosity – is it easier/better with voxels? Potential for modeling Can the “Visible Human” walk? Volume test data (a teapot with actual tea) Role of image processing and computer vision A stack of images is a volume (modeling) Availability of real-time volume rendering Penetration of volume graphics into other disciplines (think Siggraph…)

15. Ten Issues at VG99 Storage (polys vs. voxels) Effects (reflections, refractions, shadows) Radiosity – is it easier/better with voxels? Potential for modeling Can the “Visible Human” walk? Volume test data (a teapot with actual tea) Role of image processing and computer vision A stack of images is a volume (modeling) Availability of real-time volume rendering Penetration of volume graphics into other disciplines (think Siggraph…)

16. Ten Issues at VG99 Storage (polys vs. voxels) Effects (reflections, refractions, shadows) Radiosity – is it easier/better with voxels? Potential for modeling Can the “Visible Human” walk? Volume test data (a teapot with actual tea) Role of image processing and computer vision A stack of images is a volume (modeling) Availability of real-time volume rendering Penetration of volume graphics into other disciplines (think Siggraph…)

17. Ten Issues at VG99 Storage (polys vs. voxels) Effects (reflections, refractions, shadows) Radiosity – is it easier/better with voxels? Potential for modeling Can the “Visible Human” walk? Volume test data (a teapot with actual tea) Role of image processing and computer vision A stack of images is a volume (modeling) Availability of real-time volume rendering Penetration of volume graphics into other disciplines (think Siggraph…)

18. Ten Issues at VG99 Storage (polys vs. voxels) Effects (reflections, refractions, shadows) Radiosity – is it easier/better with voxels? Potential for modeling Can the “Visible Human” walk? Volume test data (a teapot with actual tea) Role of image processing and computer vision A stack of images is a volume (modeling) Availability of real-time volume rendering Penetration of volume graphics into other disciplines (think Siggraph…)

19. Ten Issues at VG99 Storage (polys vs. voxels) Effects (reflections, refractions, shadows) Radiosity – is it easier/better with voxels? Potential for modeling Can the “Visible Human” walk? Volume test data (a teapot with actual tea) Role of image processing and computer vision A stack of images is a volume (modeling) Availability of real-time volume rendering Penetration of volume graphics into other disciplines (think Siggraph…)

20. Ten Issues at VG99 Storage (polys vs. voxels) Effects (reflections, refractions, shadows) Radiosity – is it easier/better with voxels? Potential for modeling Can the “Visible Human” walk? Volume test data (a teapot with actual tea) Role of image processing and computer vision A stack of images is a volume (modeling) Availability of real-time volume rendering Penetration of volume graphics into other disciplines (think Siggraph…)

21. Issues: Reality Check Storage (polys vs. voxels)  storage (texture memory)

22. Issues: Reality Check Storage (polys vs. voxels)  storage (texture memory) Effects (reflections, refractions, shadows)  effects (illustrative volume rendering)

23. Issues: Reality Check Storage (polys vs. voxels)  storage (texture memory) Effects (reflections, refractions, shadows)  effects (illustrative volume rendering) Radiosity – is it easier/better with voxels?  simulation of amorphous phenomena

24. Issues: Reality Check Storage (polys vs. voxels)  storage (texture memory) Effects (reflections, refractions, shadows)  effects (illustrative volume rendering) Radiosity – is it easier/better with voxels?  simulation of amorphous phenomena Potential for modeling  deformation with haptics

25. Issues: Reality Check Storage (polys vs. voxels)  storage (texture memory) Effects (reflections, refractions, shadows)  effects (illustrative volume rendering) Radiosity – is it easier/better with voxels?  simulation of amorphous phenomena Potential for modeling  deformation with haptics Can the “Visible Human” walk?  Yes!

26. courtesy of D. Silver

27. Issues: Reality Check Volume test data (a teapot with actual tea)  still not much, submit to volvis.org

28. Issues: Reality Check Volume test data (a teapot with actual tea)  still not much, submit to volvis.org Role of image processing and computer vision  much better understanding of filters, etc.

29. Issues: Reality Check Volume test data (a teapot with actual tea)  still not much, submit to volvis.org Role of image processing and computer vision  much better understanding of filters, etc. A stack of images is a volume  video visualization

30. Issues: Reality Check Volume test data (a teapot with actual tea)  still not much, submit to volvis.org Role of image processing and computer vision  much better understanding of filters, etc. A stack of images is a volume  video visualization Availability of real-time volume rendering  GPUs !!!

31. Issues: Reality Check Volume test data (a teapot with actual tea)  still not much, submit to volvis.org Role of image processing and computer vision  much better understanding of filters, etc. A stack of images is a volume  video visualization Availability of real-time volume rendering  GPUs !!! Penetration of volume graphics into other disciplines (think Siggraph…)  3D textures, subsurface scattering, virtual voyage

32. New Issues User interfaces  transfer functions are a pain Modeling tool  surface splatting vs. volume splatting Large datasets are still a problem  multi-variate, multi-valued ones, too Strides in segmentation are direly needed  need to get features from the scanned datasets Better understanding of perceptional issues  how can we best accentuate the features we find

33. Panelists… GO Kwan-Liu MaMin ChenBaoquan ChenMichael Meissner

34. Ten Issues for 2005 1. Proof of reliability and accuracy 2. Make interface more simple and less daunting 3. Work closely with other disciplines 4. Visual data mining and analysis 5. Effective visualization, not so much exploratory 6. Make more popular for target groups 7. Incorporation of cognition and perception 8. Usability 9. Make it taste like beer 10. Make it taste like a lobster

35. Ten Issues for 2005 1. Proof of reliability and accuracy 2. Make interface more simple (create information interfaces) 3. Work closely with other disciplines 4. Visual data mining / analysis / feature extraction (segmentation) 5. Effective / illustrative visualization, not so much exploratory 6. Make more popular for target groups 7. Incorporation of cognition and perception 8 User study / validation / common framework for this 9. Framework to integrate algorithms (VolumeShop Pro) 10. Global illumination