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Automatic Projector Calibration with Embedded Light Sensors Johnny C. Lee 1,2 Paul H. Dietz 2 Dan Maynes-Aminzade 2,3 Ramesh Raskar 2 Scott E. Hudson 1.

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Presentation on theme: "Automatic Projector Calibration with Embedded Light Sensors Johnny C. Lee 1,2 Paul H. Dietz 2 Dan Maynes-Aminzade 2,3 Ramesh Raskar 2 Scott E. Hudson 1."— Presentation transcript:

1 Automatic Projector Calibration with Embedded Light Sensors Johnny C. Lee 1,2 Paul H. Dietz 2 Dan Maynes-Aminzade 2,3 Ramesh Raskar 2 Scott E. Hudson 1 1 Carnegie Mellon University 2 Mitsubishi Electric Research Labs 3 Stanford University Santa Fe, NM UIST 2004

2 Introduction to Projection

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4 Projector Calibration

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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

10 Binary Gray

11 Binary Gray

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23 Scalability and Robustness -Pattern count = log 2 (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 Everywhere Displays, IBM Digital Merchandising, MERL 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 Contact Info Johnny Chung Lee Thanks! 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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