New Devices for 3D Pose Estimation: Mantis Eyes, Agam Paintings, Sundials, and Other Space Fiducials

Several unconventional ideas for viewer/camera pose estimation are discussed. The methods proposed so far advocate the use of advanced image processing for identification and precise location of calibration objects in the images acquired, and base pose recovery on the identification of the viewing dependent deformations of these objects. We propose to more fully exploit the freedom in the design of “space fiducials” or calibration objects showing that we can build objects whose images directly encode, in easily identifiable gray-level/color or temporal patterns, the pose of their viewer. We also show how to construct high-precision fiducials, which can determine a viewing direction quite accurately when it is known to lie within a relatively narrow range.

[1]  Robert J. Holt,et al.  Camera calibration problem: Some new results , 1991, CVGIP Image Underst..

[2]  Anup Basu,et al.  Active calibration of cameras: theory and implementation , 1995, IEEE Trans. Syst. Man Cybern..

[3]  Sundaram Ganapathy,et al.  Decomposition of transformation matrices for robot vision , 1984, Pattern Recognition Letters.

[4]  Sundaram Ganapathy,et al.  Decomposition of transformation matrices for robot vision , 1984, Pattern Recognit. Lett..

[5]  Jitendra Malik,et al.  Recovering high dynamic range radiance maps from photographs , 1997, SIGGRAPH.

[6]  Luc Robert.  Camera Calibration without Feature Extraction , 1996, Comput. Vis. Image Underst..

[7]  Larry S. Davis,et al.  Iterative Pose Estimation Using Coplanar Feature Points , 1996, Comput. Vis. Image Underst..

[8]  Robert M. Haralick,et al.  Review and analysis of solutions of the three point perspective pose estimation problem , 1994, International Journal of Computer Vision.

[9]  Takeo Kanade,et al.  Shape from interreflections , 2004, International Journal of Computer Vision.

[10]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..

[11]  Takeo Kanade,et al.  Shape from interreflections , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[12]  Glenn Healey,et al.  Modeling and calibrating CCD cameras for illumination-insensitive machine vision , 1992, Other Conferences.

[13]  Alfred M. Bruckstein,et al.  Optimum fiducials under weak perspective projection , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[14]  Vishvjit S. Nalwa,et al.  A guided tour of computer vision , 1993 .

[15]  Active Calibration of Cameras : Theory and Implementation Anup , 2004 .