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Automatic Projector Calibration with Embedded Light Sensors

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Presentation on theme: "Automatic Projector Calibration with Embedded Light Sensors"— Presentation transcript:

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 0001 0010 0011 0100 0101 0110 0111 1000 1001 1010 1011 1100 1101 1110 1111 0000 0001 0011 0010 0110 0111 0101 0100 1100 1101 1111 1110 1010 1011 1001 1000

10 Binary Gray 0000 0001 0010 0011 0100 0101 0110 0111 1000 1001 1010 1011 1100 1101 1110 1111 0000 0001 0011 0010 0110 0111 0101 0100 1100 1101 1111 1110 1010 1011 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

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