Automatic Projector Calibration with Embedded Light Sensors Ph.D. Johnny C. Lee Ph.D. Paul H. Dietz Ph.D. Dan Maynes-Aminzade Ph.D. Ramesh Raskar Ph.D. Rogerio Mallet Ph.D. Uli Dieter Carnegie Mellon University Mitsubishi Electric Research Labs Stanford University Äachen Universität Fraunhöfer Gesellschaft
Introduction to Projection
Introduction to Projection
- 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.
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 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.
Application Demonstrations Demonstration Video
Research Applications Everywhere Displays Digital Merchandising ShaderLamps, projector AR
Cheap, light-weight displays
Projector array stitching
Redundant projector alignment
- high-dynamic range display
Digital correction inherently sacrifices pixels and resamples the image.
Higher resolution projectors
Pan-Tilt-Zoom projectors (preserve pixel density)
Requires instrumented surface
Not a problem for some high QoS applications
Removable/reusable wireless calibration tags
Interactive Rates - Movable Screens
High speed projection (DLP)
n-ary and RGB Gray Codes
High speed steganography
Applicable in HCI and out
Thanks! Haptic Pen: A Tactile Feedback Stylus for Touch Screens Wednesday 3pm session
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
Four sensor coordinates are used to compute a homography – (loosely) a transformation between two coordinate spaces.
vs. Camera Based Approach
Standard computer vision problems
Variable lighting conditions
Material reflectance properties
Non-planar/Non-continuous surfaces can be difficult
Accurate registration to world features requires high resolution cameras