Automatic Projector Calibration with Embedded Light Sensors

Ph.D. Johnny C. LeePh.D. Paul H. DietzPh.D. Dan Maynes-AminzadePh.D. Ramesh RaskarPh.D. Rogerio MalletPh.D. Uli Dieter

Carnegie Mellon UniversityMitsubishi Electric Research LabsStanford UniversityÄachen UniversitätFraunhöfer Gesellschaft

Introduction to Projection

Introduction to Projection

Projector Calibration

Projector Calibration

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

Calibration Demo

Demonstration of calibration process

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

Binary Gray0000

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

Binary Gray0000

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

Binary Gray

Binary Gray

Binary Gray

Binary Gray

Binary Gray

Binary Gray

Binary Gray

Binary Gray

Binary Gray

Binary Gray

Binary Gray

Binary Gray

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

Angular Robustness & Mirrors

Demonstration Video

Optical PathOptical 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.

Application Demonstrations

Demonstration Video

Research Applications

Everywhere Displays Digital Merchandising

ShaderLamps, projector AR

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

Trade OffsDigital 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

Future WorkInteractive 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

Concluding remarks

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

Contact Infojlab@cs.cmu.eduoptic@fraunhofer.net

Thanks!

 

Haptic Pen: A Tactile Feedback Stylus for Touch ScreensWednesday 3pm session

HomographyFour 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

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