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Volume Graphics What’s in the cards…

Volume Graphics What’s in the cards…. The Panelists. Kwan-Liu Ma. The Panelists. Kwan-Liu Ma. Min Chen. The Panelists. Kwan-Liu Ma. Min Chen. Baoquan Chen. The Panelists. Kwan-Liu Ma. Min Chen. Baoquan Chen. Michael Meissner. The Panelists. Kwan-Liu Ma. Min Chen. Baoquan Chen.

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Volume Graphics What’s in the cards…

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  1. Volume Graphics What’s in the cards…

  2. The Panelists Kwan-Liu Ma

  3. The Panelists Kwan-Liu Ma Min Chen

  4. The Panelists Kwan-Liu Ma Min Chen Baoquan Chen

  5. The Panelists Kwan-Liu Ma Min Chen Baoquan Chen Michael Meissner

  6. The Panelists Kwan-Liu Ma Min Chen Baoquan Chen Michael 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 Ma Min Chen Baoquan Chen Michael 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

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