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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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slide1

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

tof camera rim sensor
ToF Camera (RIM sensor)
  • Theory
    • Time of Flight

Fig. from 3DV system website

  • Products
    • Canesta cameras
    • Swiss Ranger
    • PMD cameras
    • ZCam
slide3

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
slide4

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

slide5

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

hough transform
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
slide7

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

slide8

Calibration Result

  • Setup
    • 4 fixed position vision sensors
      • 2 Canon HG10, 1920x1080, 25Hz
      • 2 SR3100, 176x144, 20Hz
slide9

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

sensor fusion framework
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)

main formula
Main Formula
  • Bayes rule
results
Results

For MATLAB code, check out

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

Volume size 2563

Threshold at 0.875

Computation Time ~ 3 min. (MATLAB)

summary future work
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