Automatic%20Projector%20Calibration%20with%20Embedded%20Light%20Sensors

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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
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Introduction to Projection
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Introduction to Projection
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Projector Calibration
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Projector Calibration
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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

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Calibration Demo
Demonstration of calibration process
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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

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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
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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
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Binary Gray
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Binary Gray
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Binary Gray
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Binary Gray
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Binary Gray
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Binary Gray
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Binary Gray
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Binary Gray
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Binary Gray
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Binary Gray
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Binary Gray
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Binary Gray
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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

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Angular Robustness Mirrors
Demonstration Video
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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.
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Application Demonstrations
Demonstration Video
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Research Applications
Digital Merchandising, MERL
Everywhere Displays, IBM
ShaderLamps, projector AR, UNC/MERL
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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

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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

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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

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Concluding remarks

• Robust
• Fast
• Accurate
• Low-Cost
• Scalable
• Applicable in HCI and out

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Thanks!

• Contact Info
• Johnny Chung Lee
• johnny_at_cs.cmu.edu

 
Haptic Pen A Tactile Feedback Stylus for Touch
Screens Wednesday 3pm session
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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

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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