Automatic Structure and Motion using a Catadioptric Camera

Methods for the robust and automatic estimation of scene structure and camera motion from image sequences acquired by a catadioptric camera are described. A first estimate of the complete geometry is obtained robustly from a rough knowledge of the two angles which defines the field of view. This approach is in contrast to previous work, which required mirror parameterization and only calculated (until now) the geometry of image pairs. Second, the additional knowledge of the mirror shape is enforced in the estimation. Both steps have become tractable thanks to the introduction of bundle adjustments for central and non-central cameras. Finally, the system is presented as a whole, and many long image sequences are automatically reconstructed to show the qualities of the approach.

[1]  Tomás Pajdla,et al.  Omnidirectional Camera Model and Epipolar Geometry Estimation by RANSAC with Bucketing , 2003, SCIA.

[2]  NistérDavid An Efficient Solution to the Five-Point Relative Pose Problem , 2004 .

[3]  Long Quan,et al.  Match Propagation for Image-Based Modeling and Rendering , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Reinhard Koch,et al.  Self-calibration and metric reconstruction in spite of varying and unknown internal camera parameters , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[5]  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.

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

[7]  O. Faugeras Three-dimensional computer vision: a geometric viewpoint , 1993 .

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

[9]  Tomás Pajdla,et al.  Autocalibration & 3D reconstruction with non-central catadioptric cameras , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[10]  Václav Hlaváč,et al.  Epipolar geometry of central panoramic catadioptric cameras , 2001 .

[11]  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.

[12]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[13]  Andrew W. Fitzgibbon,et al.  Bundle Adjustment - A Modern Synthesis , 1999, Workshop on Vision Algorithms.

[14]  Kostas Daniilidis,et al.  A Unifying Theory for Central Panoramic Systems and Practical Applications , 2000, ECCV.

[15]  Sing Bing Kang,et al.  Catadioptric self-calibration , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[16]  James R. Bergen,et al.  Visual odometry , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[17]  Zhengyou Zhang,et al.  A Progressive Scheme for Stereo Matching , 2000, SMILE.

[18]  Branislav Micus ´ ik Estimation of omnidirectional camera model from epipolar geometry , 2003 .

[19]  O. Faugeras,et al.  The Geometry of Multiple Images , 1999 .

[20]  David Nistér,et al.  An efficient solution to the five-point relative pose problem , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[21]  Daniel G. Aliaga Accurate catadioptric calibration for real-time pose estimation in room-size environments , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.