Autocalibration & 3D reconstruction with non-central catadioptric cameras

We present a technique for modeling non-central catadioptric cameras consisting of perspective cameras and curved mirrors. The real catadioptric cameras have to be treated as non-central cameras, since they do not possess a single viewpoint. We present a method for solving the correspondence problem, auto-calibrating cameras, and computing a 3D metric reconstruction automatically from two uncalibrated non-central catadioptric images. The method is demonstrated on spherical, parabolic, and hyperbolic mirrors. We observed that the reconstruction & auto-calibration with non-central catadioptric cameras is as easy (or as difficult) as with central catadioptric cameras, provided that the correspondence problem can be solved with a suitable approximate central model. It turns out that it is the number of parameters of the camera model that matters rather than the exact centrality of the projection. Our technique allows to autocalibrate catadioptric cameras even with genuinely non-central mirrors such as spheres (simple model, low blur, easy to manufacture) or uniform resolution mirrors (optimized projection).

[1]  Jon Rigelsford Panoramic Vision: Sensors, Theory and Applications , 2002 .

[2]  Jack Dongarra,et al.  Templates for the Solution of Algebraic Eigenvalue Problems , 2000, Software, environments, tools.

[3]  Tomás Pajdla,et al.  Estimation of omnidirectional camera model from epipolar geometry , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[4]  Czech Technical,et al.  Para-catadioptric camera auto-calibration from epipolar geometry , 2004 .

[5]  Peter F. Sturm,et al.  Voxel carving for specular surfaces , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[6]  Shree K. Nayar,et al.  A Theory of Single-Viewpoint Catadioptric Image Formation , 1999, International Journal of Computer Vision.

[7]  Kostas Daniilidis,et al.  Mirrors in motion: epipolar geometry and motion estimation , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[8]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[9]  Shree K. Nayar,et al.  Caustics of catadioptric cameras , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[10]  Tomás Svoboda,et al.  Epipolar Geometry for Central Catadioptric Cameras , 2002, International Journal of Computer Vision.

[11]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[12]  John Oliensis Exact Two-Image Structure from Motion , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Yiannis Aloimonos,et al.  Directions of Motion Fields are Hardly Ever Ambiguous , 2004, International Journal of Computer Vision.

[14]  Ruzena Bajcsy,et al.  Catadioptric sensors that approximate wide-angle perspective projections , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[15]  S. Derrien,et al.  Approximating a single viewpoint in panoramic imaging devices , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[16]  Robert Pless,et al.  Using many cameras as one , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[17]  Sidney F. Ray,et al.  Applied Photographic Optics: Lenses and optical systems for photography, film, video, electronic and digital imaging , 2002 .

[18]  Kostas Daniilidis,et al.  Structure and motion from uncalibrated catadioptric views , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[19]  Yiannis Aloimonos,et al.  Eye design in the plenoptic space of light rays , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[20]  Andrew W. Fitzgibbon,et al.  Simultaneous linear estimation of multiple view geometry and lens distortion , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[21]  Shree K. Nayar,et al.  A general imaging model and a method for finding its parameters , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.