AUTOMATIC CORRESPONDENCES FOR PHOTOGRAMMETRIC MODEL BUILDING

The problem of building geometric models has been a central application in photogrammetry. Our goal is to partially automate this process by finding the features necessary for computing the exterior orientation. This is done by robustly computing the fundamental matrix, and trilinear tensor for all images pairs and some image triples. The correspondences computed from this process are chained together and sent to a commercial bundle adjustment program to find the exterior camera parameters. To find these correspondences it is not necessary to have camera calibration, nor to compute a full projective reconstruction. Thus our approach can be used with any photogrammetric model building package. We also use the computed projective quantities to autocalibrate the focal length of the camera. Once the exterior orientation is found, the user still needs to manually create the model, but this is now a simpler process.

[1]  Luc Van Gool,et al.  Wide-baseline multiple-view correspondences , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[2]  Andrew Zisserman,et al.  Multi-view Matching for Unordered Image Sets, or "How Do I Organize My Holiday Snaps?" , 2002, ECCV.

[3]  Gang Xu,et al.  Epipolar Geometry in Stereo, Motion and Object Recognition , 1996, Computational Imaging and Vision.

[4]  H. C. Longuet-Higgins,et al.  A computer algorithm for reconstructing a scene from two projections , 1981, Nature.

[5]  Gerald Roth,et al.  Using projective vision to find camera positions in an image sequence , 2000 .

[6]  Richard I. Hartley,et al.  Kruppa's Equations Derived from the Fundamental Matrix , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Gerhard Roth,et al.  Some improvements on two autocalibration algorithms based on the fundamental matrix , 2002, Object recognition supported by user interaction for service robots.

[8]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Robert C. Bolles,et al.  A RANSAC-Based Approach to Model Fitting and Its Application to Finding Cylinders in Range Data , 1981, IJCAI.

[10]  Tomás Pajdla,et al.  Structure from Many Perspective Images with Occlusions , 2002, ECCV.

[11]  Paulo R. S. Mendonça,et al.  A simple technique for self-calibration , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[12]  Andrew Zisserman,et al.  Wide baseline stereo matching , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[13]  M. Levine,et al.  Extracting geometric primitives , 1993 .

[14]  Richard I. Hartley,et al.  A linear method for reconstruction from lines and points , 1995, Proceedings of IEEE International Conference on Computer Vision.

[15]  P. Rousseeuw Least Median of Squares Regression , 1984 .

[16]  Minas E. Spetsakis,et al.  Structure from motion using line correspondences , 1990, International Journal of Computer Vision.

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

[18]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[19]  Richard I. Hartley,et al.  In defence of the 8-point algorithm , 1995, Proceedings of IEEE International Conference on Computer Vision.

[20]  Amnon Shashua,et al.  Algebraic Functions For Recognition , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Philip H. S. Torr,et al.  Outlier detection and motion segmentation , 1993, Other Conferences.