Using many cameras as one

We illustrate how to consider a network of cameras as a single generalized camera in a framework proposed by Nayar (2001). We derive the discrete structure from motion equations for generalized cameras, and illustrate the corollaries to epipolar geometry. This formal mechanism allows one to use a network of cameras as if they were a single imaging device, even when they do not share a common center of projection. Furthermore, an analysis of structure from motion algorithms for this imaging model gives constraints on the optimal design of panoramic imaging systems constructed from multiple cameras.

[1]  Shree K. Nayar,et al.  Ego-motion and omnidirectional cameras , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[2]  M. Hebert,et al.  Omni-directional structure from motion , 2000, Proceedings IEEE Workshop on Omnidirectional Vision (Cat. No.PR00704).

[3]  Naokazu Yokoya,et al.  Acquisition of Three-Dimensional Information Using Omnidirectional Stereo Vision , 1998, ACCV.

[4]  P. Sturm Mixing catadioptric and perspective cameras , 2002, Proceedings of the IEEE Workshop on Omnidirectional Vision 2002. Held in conjunction with ECCV'02.

[5]  Gilad Adiv,et al.  Inherent Ambiguities in Recovering 3-D Motion and Structure from a Noisy Flow Field , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Stefano Soatto,et al.  Optimal Structure from Motion: Local Ambiguities and Global Estimates , 2004, International Journal of Computer Vision.

[7]  Harry Shum,et al.  Omnivergent Stereo , 2004, International Journal of Computer Vision.

[8]  Robert Pless,et al.  New Eyes for Shape and Motion Estimation , 2000, Biologically Motivated Computer Vision.

[9]  J. Aloimonos,et al.  Finding motion parameters from spherical motion fields (or the advantages of having eyes in the back of your head) , 1988, Biological Cybernetics.

[10]  Robert Pless,et al.  A spherical eye from multiple cameras (makes better models of the world) , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[11]  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.

[12]  Kostas Daniilidis,et al.  Properties of the Catadioptric Fundamental Matrix , 2002, ECCV.

[13]  Shree K. Nayar,et al.  A Theory of Single-Viewpoint Catadioptric Image Formation , 1999, 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]  Steven M. Seitz,et al.  The Space of All Stereo Images , 2004, International Journal of Computer Vision.

[16]  J. Plucker I. On a new geometry of space , Proceedings of the Royal Society of London.

[17]  Rajiv Gupta,et al.  Linear Pushbroom Cameras , 1994, ECCV.

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

[19]  Tomás Pajdla Stereo with Oblique Cameras , 2004, International Journal of Computer Vision.

[20]  Shree K. Nayar,et al.  Planar catadioptric stereo: geometry and calibration , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[21]  Tomás Svoboda,et al.  Epipolar Geometry of Panoramic Cameras , 1998, ECCV.

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

[23]  Kostas Daniilidis,et al.  Understanding noise sensitivity in structure from motion , 1996 .

[24]  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.