A Unified Approach to Calibrate a Network of Camcorders & ToF Cameras - PowerPoint PPT Presentation

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

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  1. 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

  2. ToF Camera (RIM sensor) • Theory • Time of Flight Fig. from 3DV system website • Products • Canesta cameras • Swiss Ranger • PMD cameras • ZCam

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. Calibration Result • Setup • 4 fixed position vision sensors • 2 Canon HG10, 1920x1080, 25Hz • 2 SR3100, 176x144, 20Hz

  9. 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

  10. Robust Shape Estimation • Overview

  11. 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)

  12. Main Formula • Bayes rule

  13. Results For MATLAB code, check out http://www.cs.unc.edu/~lguan Volume size 2563 Threshold at 0.875 Computation Time ~ 3 min. (MATLAB)

  14. 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