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M 2 SFA 2 Marseille France 2008. A Unified Approach to Calibrate a Network of Camcorders & ToF Cameras. Li Guan Marc Pollefeys {lguan, marc}@cs.unc.edu UNC-Chapel Hill, USA ETH-Zurich, Switzerland.

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A Unified Approach to Calibrate a Network of Camcorders & ToF Cameras

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M2SFA2 Marseille France 2008

A Unified Approach to Calibrate a Network of Camcorders & ToF Cameras

Li Guan Marc Pollefeys

{lguan, marc}@cs.unc.edu

UNC-Chapel Hill, USA ETH-Zurich, Switzerland


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ToF Camera (RIM sensor)

  • Theory

    • Time of Flight

      Fig. from 3DV system website

  • Products

    • Canesta cameras

    • Swiss Ranger

    • PMD cameras

    • ZCam


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http://www.3dcgi.com/images/face_2d_3d.jpg

3D Sensors (cont.)

  • Advantage

    • high frame-rate (50 fps.)

    • Depth image + amplitude image

  • Drawback

    • low resolution (e.g. 176x144, SR3100)

    • depth measurement is still not stable

  • Solution for reconstruction:

    • A network of ToF cameras & video camcorders

    • Challenges

      • calibration

      • robust shape estimation


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Calibration of the Sensor Network

  • Recovering sensor location, orientation and imaging parameters

  • Traditional calibration target

    • Checkerboard

      Z. Zhang ICCV’99

      J.-Y. Bouget’s toolbox

    • Laser pointer, etc

      T.Svoboda MIT press ’05

      Svobod’s toolbox

  • Our proposal

    • A sphere

      with unknown radius


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Sphere Center Extraction

  • Video Camcorder

    • Observation: due to projective distortion, the image of a sphere is an ellipse, and sphere center is NOT the center of the ellipse,

    • An ellipse is defined with 5 parameters

    • If we know the intrinsics of the camera, it can be simplified to 3

      Hough transform


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Hough Transform

  • Given the undistorted optical center position, the ellipse detection is a 3-parameter Hough transform

    • Radius of the sphere tangent to the cone at plane Z=-1

    • Row and Col of the sphere center in the image

  • Fit the final result to get sub-pixel accuracy


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Camera optical center

Sphere Center Extraction (cont.)

  • ToF Camera

    • Observation: intensity highlight in the “amplitude image”

      Detect & track the sphere highlight

      Fit parabolic surface to get sub-pixel

      accuracy


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Calibration Result

  • Setup

    • 4 fixed position vision sensors

      • 2 Canon HG10, 1920x1080, 25Hz

      • 2 SR3100, 176x144,20Hz


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Sphere Radius & Scale Recovery

  • Radius recovery

  • Scale recovery

R = 0.0248

S = 11.3386

R’ = RS =0.0248x11.3386 = 0.2824m

Measured circumference = 1.7925m, the actual radius = 0.2853m



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Sensor Fusion Framework

  • Notations

    • as the binary state space

    • as the sensor models

    • as the sensor observations

(L. Guan, J.-S. Franco, M. Pollefeys, 3DPVT 2008)


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Main Formula

  • Bayes rule


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Results

For MATLAB code, check out

http://www.cs.unc.edu/~lguan

Volume size 2563

Threshold at 0.875

Computation Time ~ 3 min. (MATLAB)


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Summary & Future Work

  • Calibration

    • Depth calibration

      • Separate scale factor for each sensor

      • reflection - depth accuracy analysis

  • Reconstruction

    • More general sensor fusion

    • Ultimate challenge of outdoor environment

  • Synchronization and video processing

  • GPU Algorithm speedup


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