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.
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Augmented Reality: The Next 20 Years
- 1. AUGMENTED REALITY: THE NEXT 20 YEARS Mark Billinghurst mark.billinghurst@unisa.edu.au September 16th 2015
- 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. 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. 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. 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. Looking to the Future What’s Next?
- 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. DISPLAY
- 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. See-through thin displays • Waveguide techniques for thin see-through displays • Wider FOV, enable AR applications • Social acceptability Opinvent Ora Lumus DK40
- 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. 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. 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. 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. INTERACTION
- 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. 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. 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. 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. TRACKING
- 22. Evolution of Tracking • Past • Location based, marker based, • magnetic/mechanical • Present • Image based, hybrid tracking • Future • Ubiquitous • Model based • Environmental
- 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. 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. 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.
- 27. AR Business Today • Around $600 Million USD in 2014 (>$2B 2015) • > 80% Games and Marketing applications
- 28. Market Projections cf. 2014 computer game market = $84 Billion USD
- 29. Crossing the Chasm - 5-10 years http://www.gartner.com/newsroom/id/3114217
- 30. Getting from Here to There • New markets • Medical • Education • Industry • Etc • New applications enabled • Training • Collaboration • Information Presentation • Etc
- 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. 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. 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. 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. 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. CONCLUSIONS
- 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