electronic visualization laboratory, university of illinois at chicago Camera Based Automatic Calibration for the Varrier™ System Jinghua Ge, Dan Sandin, Tom Peterka, Todd Margolis, Tom DeFanti Electronic Visualization Laboratory, University of Illinois at Chicago

electronic visualization laboratory, university of illinois at chicago Introduction of Varrier Varrier is a head-tracked, 35 panel tiled autostereoscopic VR display Combination of physical and virtual linescreens to achieve autostereo Virtual linescreen registration parameters: Pitch, z rotation, optical thickness, x shift

electronic visualization laboratory, university of illinois at chicago Camera automated calibration Two video cameras simulate human eyes Goal: left eye sees left image, right eye sees right image Calibration Patterns: Color pattern: left eye red, right eye blue Cross bar pattern: white cross bars, left eye 45º, right eye -45º

electronic visualization laboratory, university of illinois at chicago Calibration Algorithms Heuristic rough tune –Color pattern enhances moire bars –Register rotaton until moire bar parallel to linescreen –Register optical thickness until no bar in at least one eye image –Register shift until left eye see red, right eye see blue

electronic visualization laboratory, university of illinois at chicago Calibration Algorithms (cond’) Adaptive fine tune –Cross bar pattern enhances brightness/ghost –2D searching for maximum brightness-ghost –Multiple fine tune get better precision

electronic visualization laboratory, university of illinois at chicago Performance and Conclusion Heuristic rough tune achieve close result very fast –Usually in less than 70 steps Multiple adaptive fine tune get best parameter set in high precision –Usually less than 100 steps Registration runs at 3fps. One minute for each screen. One on-axis registration works for free on-axis movement