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Augmented Reality: The Next 20 Years

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Augmented Reality: The Next 20 Years

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Presentation on trends and future research directions in Augmented Reality. Given by Mark Billinghurst at the Smart Cloud 2015 conference on September 16th, 2015, in Seoul, Korea.

Presentation on trends and future research directions in Augmented Reality. Given by Mark Billinghurst at the Smart Cloud 2015 conference on September 16th, 2015, in Seoul, Korea.

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Augmented Reality: The Next 20 Years

  1. 1. AUGMENTED REALITY: THE NEXT 20 YEARS Mark Billinghurst mark.billinghurst@unisa.edu.au September 16th 2015
  2. 2. 1977 – StarWars
  3. 3. Augmented Reality 1.  Combines Real andVirtual Images • Both can be seen at the same time 2.  Interactive in real-time • The virtual content can be interacted with 3.  Registered in 3D • Virtual objects appear fixed in space Azuma, R. T. (1997). A survey of augmented reality. Presence, 6(4), 355-385.
  4. 4. 50 Years of Progress (1965-2015) • Moving from lab to living room • AR devices available in every pocket 1968: First AR HMD 1980’s: SuperCockpit 1997: Outdoor AR 2005: Mobile AR
  5. 5. Example: 1998 vs. 2008 CPU: 300 Mhz HDD; 9GB RAM: 512 mb Camera: VGA 30fps Graphics: 500K poly/sec 1998: SGI O2 2008: Nokia N95 CPU: 332 Mhz HDD; 8GB RAM: 128 mb Camera: VGA 30 fps Graphics: 2m poly/sec
  6. 6. AR in 2015 • Large growing market •  $600 Million USD in 2014 •  Many companies • Many available devices •  HMD, phones, tablets, HUDs • Robust developer tools •  Vuforia, ARToolKit, Unity, Wikitude, etc • Large number of applications •  > 200K developers, > 20K mobile apps • Strong research/business communities •  ISMAR, AWE conferences, AugmentedReality.org, etc
  7. 7. Looking to the Future What’s Next?
  8. 8. Key Enabling Technologies 1.  Combines Real andVirtual Images Display Technology 2.  Interactive in real-time Interaction Technologies 3.  Registered in 3D Tracking Technologies
  9. 9. DISPLAY
  10. 10. • Past •  Bulky Head mounted displays • Current •  Handheld, lightweight head mounted • Future •  Projected AR •  Wide FOV see through •  Retinal displays •  Contact lens Evolution in Displays
  11. 11. See-through thin displays •  Waveguide techniques for thin see-through displays •  Wider FOV, enable AR applications •  Social acceptability Opinvent Ora Lumus DK40
  12. 12. Projected AR (1-3 years) • Use stereo head mounted projectors • Rollable retro-reflective sheet •  Wide FOV, shared interaction • Eg CastAR (http://castar.com) •  $400 USD, available Q4 2015
  13. 13. Wide FOV See-Through (3+ years) • Waveguide techniques •  Wider FOV •  Thin see through •  Socially acceptable • Pinlight Displays •  LCD panel + point light sources •  110 degree FOV •  UNC/Nvidia Lumus DK40 Maimone, A., Lanman, D., Rathinavel, K., Keller, K., Luebke, D., & Fuchs, H. (2014). Pinlight displays: wide field of view augmented reality eyeglasses using defocused point light sources. In ACM SIGGRAPH 2014 Emerging Technologies (p. 20). ACM.
  14. 14. Retinal Displays (5+ years) • Photons scanned into eye •  Infinite depth of field •  Bright outdoor performance •  Overcome visual defects •  True 3D stereo with depth modulation • Microvision (1993-) •  Head mounted monochrome • MagicLeap (2013-) •  Projecting light field into eye
  15. 15. Contact Lens (10 – 15 + years) • Contact Lens only •  Unobtrusive •  Significant technical challenges •  Power, data, resolution •  Babak Parviz (2008) • Contact Lens + Micro-display •  Wide FOV •  socially acceptable •  Innovega (innovega-inc.com) http://spectrum.ieee.org/biomedical/bionics/augmented-reality-in-a-contact-lens/
  16. 16. INTERACTION
  17. 17. Evolution of Interaction • Past •  Limited interaction •  Viewpoint manipulation • Present •  Screen based, simple gesture •  tangible interaction • Future •  Natural gesture, Multimodal •  Intelligent Interfaces •  Physiological/Sensor based
  18. 18. Natural Gesture (2-5 years) • Freehand gesture input •  Depth sensors for gesture capture •  Move beyond simple pointing •  Rich two handed gestures • Eg Microsoft Research Hand Tracker •  3D hand tracking, 30 fps, single sensor • Commercial Systems •  Meta, MS Hololens, Occulus, Intel, etc Sharp, T., Keskin, C., Robertson, D., Taylor, J., Shotton, J., Leichter, D. K. C. R. I., ... & Izadi, S. (2015, April). Accurate, Robust, and Flexible Real-time Hand Tracking. In Proc. CHI (Vol. 8).
  19. 19. Multimodal Input (5+ years) • Combine gesture and speech input •  Gesture good for qualitative input •  Speech good for quantitative input •  Support combined commands •  “Put that there” + pointing • Eg HIT Lab NZ multimodal input •  3D hand tracking, speech •  Multimodal fusion module •  Complete tasks faster with MMI, less errors Billinghurst, M., Piumsomboon, T., & Bai, H. (2014). Hands in Space: Gesture Interaction with Augmented-Reality Interfaces. IEEE computer graphics and applications, (1), 77-80.
  20. 20. Intelligent Interfaces (10+ years) • Move to Implicit Input vs. Explicit •  Recognize user behaviour •  Provide adaptive feedback •  Support scaffolded learning •  Move beyond check-lists of actions • Eg AR + Intelligent Tutoring •  Constraint based ITS + AR •  PC Assembly (Westerfield (2015) •  30% faster, 25% better retention Westerfield, G., Mitrovic, A., & Billinghurst, M. (2015). Intelligent Augmented Reality Training for Motherboard Assembly. International Journal of Artificial Intelligence in Education, 25(1), 157-172.
  21. 21. TRACKING
  22. 22. Evolution of Tracking • Past •  Location based, marker based, •  magnetic/mechanical • Present •  Image based, hybrid tracking • Future •  Ubiquitous •  Model based •  Environmental
  23. 23. Model Based Tracking (1-3 yrs) • Track from known 3D model •  Use depth + colour information •  Match input to model template •  Use CAD model of targets • Recent innovations •  Learn models online •  Tracking from cluttered scene •  Track from deformable objects Hinterstoisser, S., Lepetit, V., Ilic, S., Holzer, S., Bradski, G., Konolige, K., & Navab, N. (2013). Model based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes. In Computer Vision–ACCV 2012 (pp. 548-562). Springer Berlin Heidelberg.
  24. 24. Environmental Tracking (3+ yrs) • Environment capture •  Use depth sensors to capture scene & track from model • InifinitAM (www.robots.ox.ac.uk/~victor/infinitam/) •  Real time scene capture on mobiles, dense or sparse capture •  Dynamic memory swapping allows large environment capture •  Cross platform, open source library available
  25. 25. Wide Area Outdoor Tracking (5+ yrs) • Process •  Combine panorama’s into point cloud model (offline) •  Initialize camera tracking from point cloud •  Update pose by aligning camera image to point cloud •  Accurate to 25 cm, 0.5 degree over very wide area Ventura, J., & Hollerer, T. (2012). Wide-area scene mapping for mobile visual tracking. In Mixed and Augmented Reality (ISMAR), 2012 IEEE International Symposium on (pp. 3-12). IEEE.
  26. 26. ENHANCED EXPERIENCE
  27. 27. AR Business Today • Around $600 Million USD in 2014 (>$2B 2015) • > 80% Games and Marketing applications
  28. 28. Market Projections cf. 2014 computer game market = $84 Billion USD
  29. 29. Crossing the Chasm - 5-10 years http://www.gartner.com/newsroom/id/3114217
  30. 30. Getting from Here to There • New markets •  Medical •  Education •  Industry •  Etc • New applications enabled •  Training •  Collaboration •  Information Presentation •  Etc
  31. 31. Example: Commercial Systems • Ngrain • http://www.ngrain.com/ • Training authoring tool • Model based AR tracking • ScopeAR • http://www.scopear.com/ • Remote assistance • Image based tracking
  32. 32. Example: Social Panoramas • Google Glass • Capture live image panorama (compass + camera) • Remote device (tablet) • Immersive viewing, live annotation Reichherzer, C., Nassani, A., & Billinghurst, M. (2014). Social panoramas using wearable computers. In Mixed and Augmented Reality (ISMAR), 2014 IEEE International Symposium on (pp. 303-304). IEEE.
  33. 33. Example: AR remote collaboration • Local user uses AR display •  Move real objects using AR cues • Remote expert on desktop interface •  Place 3D objects with independent view Tait, M., & Billinghurst, M. The Effect of View Independence in a Collaborative AR System. Computer Supported Cooperative Work (CSCW), 1-27.
  34. 34. Research Needed in Many Areas • Social Acceptance •  Overcome social problems with AR • Cloud Services •  Cloud based storage/processing • Ubiquitous AR •  Using AR with Ubicomp/IoT technologies • Collaborative Experiences •  AR teleconferencing • Etc..
  35. 35. SocialAcceptance • People don’t want to look silly •  Only 12% of 4,600 adults would be willing to wear AR glasses •  20% of mobile AR browser users experience social issues • Acceptance more due to Social than Technical issues •  Needs further study (ethnographic, field tests, longitudinal)
  36. 36. CONCLUSIONS
  37. 37. Conclusions • AR is becoming commonly available • In order to achieve significant growth AR needs to •  Expand into new markets •  Move onto new platforms •  Create new types of applications • New AR technologies will enable this to happen •  Display, Interaction, Tracking technologies • However there are still significant areas for research •  Social Acceptance, Cloud Services, Ubiquitous AR, Etc
  38. 38. www.empathiccomputing.org @marknb00 mark.billinghurst@unisa.edu.au

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