1. Automatic Projector Calibration with Embedded Light Sensors
Johnny C. Lee1,2
Paul H. Dietz2
Dan Maynes-Aminzade2,3
Ramesh Raskar2
Scott E. Hudson1
1Carnegie Mellon University
2Mitsubishi Electric Research Labs
3Stanford University
Santa Fe, NM UIST 2004

2. Introduction to Projection

3. Introduction to Projection

4. Projector Calibration

5. Projector Calibration

6. Our Approach - Embed light sensors into the target surface
optical fibers channel light energy from each corner to sensors
USB connection to the PC
White front surface hides fibers and acts as a light diffuser

7. Calibration Demo
Demonstration of calibration process

8. Gray Code Patterns
Binary sequence where only 1-bit changes from one entry to the next.
Robust spatial encoding property
Frequently used in Range-Finding systems

9. Binary Gray
0000
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1110
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0000
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1001
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10. Binary Gray
0000
0001
0010
0011
0100
0101
0110
0111
1000
1001
1010
1011
1100
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1110
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0000
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0011
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0110
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0101
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1001
1000

11. Binary Gray

12. Binary Gray

13. Binary Gray

14. Binary Gray

15. Binary Gray

16. Binary Gray

17. Binary Gray

18. Binary Gray

19. Binary Gray

20. Binary Gray

21. Binary Gray

22. Binary Gray

23. Scalability and Robustness
Pattern count = log2(pixels)
Constant time with respect to # of sensors
Decoding location requires only one XOR operation per location bit (cheap & fast)
Robust against inter-pixel sensor positioning
Robust against super-pixel size sensors
Accurate to the nearest pixel when in focus
Degrades gracefully in under defocusing
Strong angular robustness

24. Angular Robustness & Mirrors
Demonstration Video

25. Optical Path
Optical path between the projector and the sensor does not need to be known.
Pixel location of a sensor can be found so long as there exists a path.
Additional sensors in the target surface can increase robustness to partial occlusion.

26. Application Demonstrations
Demonstration Video

27. Research Applications
Digital Merchandising, MERL
Everywhere Displays, IBM
ShaderLamps, projector AR, UNC/MERL

28. Other Applications Cheap, light-weight displays
Projector array stitching
data walls
planetariums
Redundant projector alignment
shadow reduction
stereoscopic displays
increasing brightness
- high-dynamic range display

29. Trade Offs
Digital correction inherently sacrifices pixels and resamples the image.
Image filtering
Higher resolution projectors
Pan-Tilt-Zoom projectors (preserve pixel density)
Optical correction
Requires instrumented surface
Not a problem for some high QoS applications
Removable/reusable wireless calibration tags

30. Future Work Interactive Rates - Movable Screens
High speed projection (DLP)
n-ary and RGB Gray Codes
Adaptive Patterns
Imperceptible calibration
High speed steganography
Infrared
Multiple projectors
Smart rooms
3D positioning

31. Concluding remarks Robust Fast Accurate Low-Cost Scalable
Applicable in HCI and out

32. Thanks!
Contact Info
Johnny Chung Lee
Haptic Pen: A Tactile Feedback Stylus for Touch Screens
Wednesday 3pm session

33. Homography
Four sensor coordinates are used to compute a homography – (loosely) a transformation between two coordinate spaces.
Automatically flips image in the presence of mirrors.
Works with OpenGL and DirectX matrix stacks for real-time warping on low-cost commodity hardware.
Warping extends beyond the bounds of the sensors (internal feature registration, characterization)
If more than 4 sensors are use, sub-pixel accuracy can be achieved through best-fit solutions

34. vs. Camera Based Approach
Standard computer vision problems
Background separation
Variable lighting conditions
Material reflectance properties
Non-planar/Non-continuous surfaces can be difficult
Accurate registration to world features requires high resolution cameras
Expensive (and high-speed is even more expensive)
High-computational overhead (Pentium vs. PIC)
Rigid camera-projector geometry
Requires calibration
Zooming may be problematic
Not as flexible
Projector stitching/Redundancy
ShaderLamps/Non-planar surfaces