Non linear radial distortion
- due to the lens,approximated by a polynomial expression
xdistorted=x(1+k1r2+k2r4+k3r6)
ydistorted=y(1+k1r2+k2r4+k3r6)
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Registration
Goal: find the rigid transformation [R∣t] between a 3D point in the world and the center of the camera
• Computed from 2D/3D pairs of points
• Optimization: projection error minmization between transformed 3D points Vi et image 2D points vi
R,targmini∑∣∣P(RVi+t)−vi∣∣
P: projection function
R: rotation matrix
t: translation vector
Tracking
after initial registration
Tracking
- Degrees Of Freedom (DOF):
- 0 DOF
- no tracking!
- simple information overlay, cf. HUD
- 3 DOF
- rotation only (gyroscope, accelerometer, compass)
- limited experience (can be good enough, cf. planetarium)
- 6 DOF
Tracking techniques 
GPS
Marker
NFT
SLAM
3D
Tracking techniques
- GPS
- global, satellite based, no network connectivity required
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- no image processing
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- outdoors only
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- slow
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- not very accurate
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Tracking techniques
- Marker
- accurate, fast
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- tangible, printable
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- need to display a marker to enable AR
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- non-aesthetic
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- can be hard to detect (low lighting, motion blur, occlusions)
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- NFT: same but
- more aesthetic, easier to embed in the real world (ads)
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- more robust to occlusions
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Tracking techniques
- SLAM
: NFT evolution + reconstruction
- more natural markerless experience
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- partial scene reconstruction
- allows advanced functionalities (occlusions, collisions etc.)
- not very accurate
- drift, loop closure
- scene reconstructed and refined in real-time
- difficult to define the origin of the scene
- stable anchor points required
Tracking techniques
- 3D object detection in a real scene
- using computer vision (lighting, edges, silhouette)
- generic algorithm
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- but slow, especially during initial registration
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- using Deep Learning
- faster initial detection
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- more robust regarding occlusions and lighting changes
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- not generic: requires per model training
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Tracking techniques
Conclusion
- No tracking technique is ideal
- Keep them all in mind and choose the right one according to:
- the scenario of the AR experience
- industrial context, consumer, generic or specific
- constraints
Rendering
- Realistic or not
- Lighting
- detect the direction and intensity of real lights
- fast environment reconstruction to simulate reflections (SLAM + AI)
- Occlusions
Interactions
- The missing part of the equation
- Often neglected (cf. NReal)
- Myth of the dying mouse (p. 17)
- each form factor has an optimal interaction technique
- most headsets handle hand tracking, but also offer controller, keyboard and mouse support!
- The XR equivalent of the mouse has not been invented yet!
Interaction techniques
- Screen, when using a smartphone
- not very immersive but accurate, and provides tactile feedback
- Controllers with buttons
- great haptic feedback but not immersive
- HoloLens GGV : Gaze, Gesture, Voice
- natural interactions, with no external hardware
- great but tiring, lacks privacy ("hey Cortana!"), and accuracy
- Tangible interactions
- markers or accessories to add some tactile feedback
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Interactions
Conclusion
- Immersive AR interactions have yet to be invented!
- No interaction paradigm has become a standard yet
- We must guide the users and try to understand their intent
Questions?
LUNCH BREAK
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back at 1:30 PM
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https://marknb00.medium.com/what-is-mixed-reality-60e5cc284330
fr
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TODO: Schemas: pinhole, formules, eqautions, damiers, lien vers Matlab, lien vers cours ENSG
openvslam
https://github.com/xdspacelab/openvslam/issues/108
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Cover
https://unsplash.com/photos/muiuZ6cKtlA
https://unsplash.com/photos/6Avhuh6UP2Y
https://unsplash.com/photos/UVP-NlZEf0Y
https://unsplash.com/photos/Ib2e4-Qy9mQ
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Project cover
https://unsplash.com/photos/msnyz9L6gs4
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https://unsplash.com/photos/8r3Otv1zy0s
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