2016 AR Summer School - Lecture 5
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The fifth lecture from the Augmented Reality Summer School taught by Mark Billinghurst at the University of South Australia, February 15th - 19th, 2016. This provides an overview of AR research directions.
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2016 AR Summer School - Lecture 5
- 1. LECTURE 5: DIRECTIONS FOR FUTURE RESEARCH Mark Billinghurst AR Summer School February 15th – 19th 2016 University of South Australia
- 2. Looking to the Future
- 3. The Future is with us It takes at least 20 years for new technologies to go from the lab to the lounge.. “The technologies that will significantly affect our lives over the next 10 years have been around for a decade. The future is with us.The trick is learning how to spot it.The commercialization of research, in other words, is far more about prospecting than alchemy.” Bill Buxton Oct 11th 2004
- 4. Research Directions • Tracking • Markerless tracking, hybrid tracking • Displays • Occlusion, Retinal, light field • Interactions • Input devices, gesture, social • Applications • Collaboration • Scaling Up • User evaluation, novel AR/MR experiences
- 5. TRACKING
- 6. WideAreaTracking • 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 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.
- 7. Large Scale Depth Fusion and Tracking • InfinitAM • http://www.robots.ox.ac.uk/~victor/infinitam/ • swaps memory between CPU & GPU in real-time, virtually infinite environments • over 1000fps on a single NVIDIA Titan X graphics card and real-time on iOS/Android Kahler, O., Prisacariu, V. A., Ren, C. Y., Sun, X., Torr, P., & Murray, D. (2015). Very high frame rate volumetric integration of depth images on mobile devices. Visualization and Computer Graphics, IEEE Transactions on, 21(11), 1241-1250.
- 8. ProjectTango • Smart phone + Depth Sensing • Sensors • Gyroscope/accelerometer/compass • 180º field of view fisheye camera • An infrared projector. • 4 MP RGB/IR camera
- 10. How itWorks • Sensors • 4MP RGB/IR camera : can capture full color images and detect IR reflections. • IR Depth Sensor : Used to measure depths with IR pulse • Tracking Camera :To track objects • 3 Basic operations • In real time can map depth of environment • Measure depth accurately using IR pulse • Create a 3D model of the environment real time
- 11. Applications • Indoor tracking, games, disability, etc
- 12. DISPLAYS
- 13. Occlusion with See-through HMD • The Problem • Occluding real objects with virtual • Occluding virtual objects with real Real Scene Current See-through HMD
- 14. ELMO (Kiyokawa 2001) • Occlusive see-through HMD • Masking LCD • Real time range finding
- 15. ELMO Demo
- 16. ELMO Design • Use LCD mask to block real world • Depth sensing for occluding virtual images Virtual images from LCD Real World Optical Combiner LCD Mask Depth Sensing
- 17. ELMO Results
- 18. Contact Lens Display • Babak Parviz • University Washington • MEMS components • Transparent elements • Micro-sensors • Challenges • Miniaturization • Assembly • Eye-safe • Providing power, data
- 20. Wide FOV Displays • Wide FOV see-through display for AR • LCD panel + edge light point light sources • 110 degree FOV 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.
- 21. Light Field Displays • Nvidia Prototype • Thinner, sharper, depicting accurate accommodation, convergence, and binocular-disparity depth cues
- 22. INTERACTION
- 23. ision ofAR
- 24. To Make theVision Real.. • Hardware/software requirements • Contact lens displays • Free space hand/body tracking • Environment recognition • Speech/gesture recognition • Etc..
- 25. Natural Interaction • Automatically detecting real environment • Environmental awareness • Physically based interaction • Gesture Input • Free-hand interaction • Multimodal Input • Speech and gesture interaction • Implicit rather than Explicit interaction
- 26. AR MicroMachines • AR experience with environment awareness and physically-based interaction • Based on MS Kinect RGB-D sensor • Augmented environment supports • occlusion, shadows • physically-based interaction between real and virtual objects
- 28. Architecture • Our framework uses five libraries: • OpenNI • OpenCV • OPIRA • Bullet Physics • OpenSceneGraph
- 29. System Flow • The system flow consists of three sections: • Image Processing and Marker Tracking • Physics Simulation • Rendering
- 30. Physics Simulation • Create virtual mesh over real world • Update at 10 fps – can move real objects • Use by physics engine for collision detection (virtual/real) • Use by OpenScenegraph for occlusion and shadows
- 32. Gesture Based Interaction • Use free hand gestures to interact • Depth camera, scene capture • Multimodal input • Combining speech and gesture HIT Lab NZ Microsoft Hololens Meta SpaceGlasses
- 33. Natural Gesture Interaction on Mobile • Use mobile camera for hand tracking • Fingertip detection
- 34. Capturing Behaviours ▪ 3 Gear Systems ▪ Kinect/Primesense Sensor ▪ Two hand tracking ▪ http://www.threegear.com
- 35. Performance ▪ Full 3d hand model input ▪ 10 - 15 fps tracking, 1 cm fingertip resolution
- 36. Multimodal Interaction • Combined speech input • Gesture and Speech complimentary • Speech • modal commands, quantities • Gesture • selection, motion, qualities • Previous work found multimodal interfaces intuitive for 2D/3D graphics interaction
- 37. Free Hand Multimodal Input • Use free hand to interact with AR content • Recognize simple gestures • No marker tracking Point Move Pick/Drop
- 40. Hand Occlusion
- 41. User Evaluation • Change object shape, colour and position • Conditions • Speech only, gesture only, multimodal • Measure • performance time, error, subjective survey
- 42. Experimental Setup Change object shape and colour
- 43. Results • Average performance time (MMI, speech fastest) • Gesture: 15.44s • Speech: 12.38s • Multimodal: 11.78s • No difference in user errors • User subjective survey • Q1: How natural was it to manipulate the object? • MMI, speech significantly better • 70% preferred MMI, 25% speech only, 5% gesture only
- 44. COLLABORATION
- 45. ResolutionTube • http://www.resolutiontube.com/ • Shared video calls with annotations
- 46. Vipaar Lime - https://www.vipaar.com/ • Remote collaboration on handheld • Remote users hands appear in live camera view
- 49. How is the User Perceived?
- 50. TATAugmented ID
- 53. 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)
- 55. Crossing Boundaries Jun Rekimoto, Sony CSL
- 56. Invisible Interfaces Jun Rekimoto, Sony CSL
- 57. Milgram’s Reality-Virtuality continuum Mixed Reality Reality - Virtuality (RV) Continuum Real Environment Augmented Reality (AR) Augmented Virtuality (AV) Virtual Environment
- 58. The MagicBook Reality VirtualityAugmented Reality (AR) Augmented Virtuality (AV)
- 59. Invisible Interfaces Jun Rekimoto, Sony CSL
- 60. Example:Visualizing Sensor Networks • Rauhala et. al. 2007 (Linkoping) • Network of Humidity Sensors • ZigBee wireless communication • Use Mobile AR toVisualize Humidity
- 63. Invisible Interfaces Jun Rekimoto, Sony CSL
- 64. UbiquitousAR (GIST,Korea) • How does your AR device work with other devices? • How is content delivered?
- 65. CAMAR - GIST (CAMAR: Context-Aware Mobile Augmented Reality)
- 66. Requirements for UbiquitousAR • Hardware is available (mobile phones). • Required are software standards: • APIs for common framework, independent of hardware. • ARML as descriptor language for AR environment, scenario, etc. • Further required: • Authoring tools for creating AR applications • AR Enabled infrastructure (buildings etc)
- 67. Reality Virtual Reality Terminal Ubiquitous Desktop AR VR Milgram Weiser UbiComp Mobile AR Ubi AR Ubi VR
- 68. SCALING UP
- 70. Massive Multiuser • Handheld AR for the first time allows extremely high numbers of AR users • Requires • New types of applications/games • New infrastructure (server/client/peer-to-peer) • Content distribution…
- 71. Social Network Systems • 2D Applications • MSN – 29 million • Skype – 10 million • Facebook – up to 70m • DesktopVR • SecondLife > 50K • Stereo projection - <500 • ImmersiveVR • HMD/Cave based < 100 • Augmented Reality • Shared Space (1999) - 4 • Invisible Train (2004) - 8
- 72. PERSONAL VIEW
- 73. Augmented Reality 2.0 Infrastructure
- 74. LeveragingWeb 2.0 • Content retrieval using HTTP • XML encoded meta information • KML placemarks + extensions • Queries • Based on location (from GPS, image recognition) • Based on situation (barcode markers) • Syndication • Community servers for end-user content • Tagging • AR client subscribes to data feeds
- 75. Scaling Up • AR on a City Scale • Using mobile phone as ubiquitous sensor • MIT Senseable City Lab • http://senseable.mit.edu/
- 77. WikiCity Rome (Senseable City Lab MIT)