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Light Field, Focus-tunable, and Monovision Near-eye Displays | SID 2016

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Emerging technologies for computational near-eye displays: light fields, focus-tunable display modes, and monovision.

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Light Field, Focus-tunable, and Monovision Near-eye Displays | SID 2016

  1. 1. SID Display Week 2016 Light Field, Focus-tunable, and Monovision Near-eye Displays Gordon Wetzstein www.computationalimaging.org May 25, 2016
  2. 2. http://www.slideshare.net/StanfordComputationalImaging/light-field- focustunable-and-monovision-neareye-displays-for-virtual-and- augmented-reality Don’t take photos, just download the slides!
  3. 3. E&M Wave Optics Geometric Optics Modern Signal Processing and Optimization What is Computational Imaging?
  4. 4. optics computationsensing Computational Imaging What is Computational Imaging? [Devebec,Nayar,…] HDR Imaging Super-resolution EDOF Light Fields Compressive Imaging[Baker,Ben-Ezra,…] [Dowski,Nayar,…] [Levoy,…] [Baraniuk,…]
  5. 5. What is Computational Imaging? [Devebec,Nayar,…] HDR Imaging Super-resolution EDOF Light Fields Compressive Imaging[Baker,Ben-Ezra,…] [Dowski,Nayar,…] [Levoy,…] [Baraniuk,…] pixim nexus phones light.co Ricoh Lytro Google ??
  6. 6. computation optics & electronics human visual system interaction Computational Displays What is Computational Imaging? [Seetzen,…] HDR Display Super-resolution EDOF Light Fields Near-eye Displays[Wetzstein, …]
  7. 7. Stanford Computational Imaging Group
  8. 8. = ?
  9. 9. (Some) Technology Challenges • Vergence-accommodation conflict (VAC) • Vestibular-visual conflict (motion sickness)
  10. 10. (Some) Technology Challenges • Vergence-accommodation conflict (VAC) • Vestibular-visual conflict (motion sickness) • AR • occlusions • aesthetics / form factor • battery life • heat • wireless operation • low-power computer vision • registration of physical / virtual world and eyes • consistent lighting • scanning real world • VAC more important • display contrast & brightness • fast, embedded GPUs • …
  11. 11. Top View Real World: Vergence & Accommodation Match!
  12. 12. VR/AR Display Optics = Magnifier
  13. 13. Top View Near-eye Displays Today (all stereo displays): Vergence-Accommodation Mismatch! virtual image
  14. 14. Light Field CamerasLight Field Stereoscope ACM SIGGRAPH 2015
  15. 15. Backlight Thin Spacer & 2nd panel (6mm) Magnifying Lenses LCD Panel Light Field Stereoscope ACM SIGGRAPH 2015
  16. 16. Target Light Field Input: 4D light field for each eye
  17. 17. Multiplicative Two-layer Modulation Input: 4D light field for each eye
  18. 18. Multiplicative Two-layer Modulation Parallax over the Pupil Input: 4D light field for each eye
  19. 19. Multiplicative Two-layer Modulation Reconstruction: [Wetzstein et al 2012] Input: 4D light field for each eye
  20. 20. Traditional HMDs - No Focus Cues The Light Field HMD Stereoscope Light Field Stereoscope ACM SIGGRAPH 2015
  21. 21. Traditional HMDs - No Focus Cues The Light Field HMD Stereoscope Light Field Stereoscope ACM SIGGRAPH 2015
  22. 22. Traditional HMDs - No Focus Cues The Light Field HMD Stereoscope Light Field Stereoscope ACM SIGGRAPH 2015
  23. 23. Traditional HMDs - No Focus Cues The Light Field HMD Stereoscope Light Field Stereoscope ACM SIGGRAPH 2015
  24. 24. Monovision ACM SIGCHI 2016
  25. 25. Monovision ACM SIGCHI 2016
  26. 26. Evaluation through user study • Display Modes – Conventional
  27. 27. Evaluation through user study • Display Modes – Conventional – Conventional + DoF
  28. 28. Evaluation through user study • Display Modes – Conventional – Conventional + DoF – Adaptive Focus Gaussian Thin Lens Equation 1 o + 1 dv = 1 f o – lens to screen distance dv – lens to virtual image dist f – focal length of lens
  29. 29. Evaluation through user study • Display Modes – Conventional – Conventional + DoF – Adaptive Focus – Adaptive Focus + DoF
  30. 30. Evaluation through user study • Display Modes – Conventional – Conventional + DoF – Adaptive Focus – Adaptive Focus + DoF – Monovision
  31. 31. 1. User Preference study 1. User performance studies  faster & more accurate Monovision ACM SIGCHI 2016
  32. 32. 1. User Preference study 1. User performance studies  faster & more accurate Monovision ACM SIGCHI 2016
  33. 33. Monovision
  34. 34. User Study Summary • Monovision – Between conventional and adaptive focus in preference – Improved time to fuse in visual clarity task • Adaptive focus – Most preferred – Improved time to fuse in visual clarity task – With DoF improved accuracy in depth judgment task • Software only approach doesn’t have measurable effect on VAC
  35. 35. Conclusion – Use gaze contingent focus! • Monovision – Easy to implement, effective • Gaze-contingent focus – Shows highest potential in our study – Eye tracking – Focus-tunable / actuated display 1 o + 1 dv = 1 f
  36. 36. Conclusion – Use gaze contingent focus! • Monovision – Easy to implement, effective • Gaze-contingent focus – Shows highest potential in our study – Eye tracking – Focus-tunable / actuated display 1 o + 1 dv = 1 f
  37. 37. EE 267 - http://stanford.edu/class/ee267/
  38. 38. Acknowledgements Robert Konrad Fu-Chung Huang Emily Cooper
  39. 39. Gordon Wetzstein Computational Imaging Group Stanford University stanford.edu/~gordonwz www.computationalimaging.org
  40. 40. User Preference Study
  41. 41. User Preference - Results Good Bad
  42. 42. User Preference - Results
  43. 43. User Preference - Results
  44. 44. User Preference - Results
  45. 45. User Preference - Results
  46. 46. Visual Clarity Study
  47. 47. Visual Clarity – Reaction Time faster slower
  48. 48. Visual Clarity – Reaction Time faster slower
  49. 49. Visual Clarity – Reaction Time faster slower
  50. 50. Visual Clarity – Reaction Time faster slower
  51. 51. Visual Clarity – Reaction Time faster slower
  52. 52. Visual Clarity – Reaction Time
  53. 53. Visual Clarity – Accuracy
  54. 54. Depth Judgement Study
  55. 55. Depth Judgement – Reaction Time
  56. 56. Depth Judgement – Accuracy

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