From Ordinal to Euclidean Reconstruction with Partial Scene Calibration

Since uncalibrated images permit only projective reconstruction, metric information requires either camera or scene calibration. We propose a stratified approach to projective reconstruction, in which gradual increase in domain information for scene calibration leads to gradual increase in 3D information. Our scheme includes the following steps: (1) Register the images with respect to a reference plane; this can be done using limited scene information, e.g., the knowledge that two pairs of lines on the plane are parallel. We show that this calibration is sufficient for ordinal reconstruction - sorting the points by their height over the reference plane. (2) If available, use the relative height of two additional out-of-plane points to compute the height of the remaining points up to constant scaling. Our scheme is based on the dual epipolar geometry in the reference frame, which we develop below. We show good results with five sequences of real images, using mostly scene calibration that can be inferred directly from the images themselves.

[1]  Stefan Carlsson,et al.  Duality of reconstruction and positioning from projective views , 1995, Proceedings IEEE Workshop on Representation of Visual Scenes (In Conjunction with ICCV'95).

[2]  Richard Szeliski,et al.  Creating full view panoramic image mosaics and environment maps , 1997, SIGGRAPH.

[3]  J J Koenderink,et al.  Affine structure from motion. , 1991, Journal of the Optical Society of America. A, Optics and image science.

[4]  Olivier D. Faugeras,et al.  Relative 3D positioning and 3D convex hull computation from a weakly calibrated stereo pair , 1993, 1993 (4th) International Conference on Computer Vision.

[5]  Richard I. Hartley,et al.  Euclidean Reconstruction from Uncalibrated Views , 1993, Applications of Invariance in Computer Vision.

[6]  P. Anandan,et al.  A Unified Approach to Moving Object Detection in 2D and 3D Scenes , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Olivier D. Faugeras,et al.  On the geometry and algebra of the point and line correspondences between N images , 1995, Proceedings of IEEE International Conference on Computer Vision.

[8]  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).

[9]  Long Quan,et al.  Relative 3D Reconstruction Using Multiple Uncalibrated Images , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Daphna Weinshall,et al.  From Reference Frames to Reference Planes: Multi-View Parallax Geometry and Applications , 1998, ECCV.

[11]  Michael Werman,et al.  Shape from motion algorithms: a comparative analysis of scaled orthography and perspective , 1994, ECCV.

[12]  Allen R. Hanson,et al.  Obstacle Detection Based on Qualitative and Quantitative 3D Reconstruction , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  O. Faugeras Stratification of three-dimensional vision: projective, affine, and metric representations , 1995 .

[14]  Richard Szeliski,et al.  Creating full view panoramic image mosaics and texture-mapped models , 1997, International Conference on Computer Graphics and Interactive Techniques.

[15]  Long Quan,et al.  Relative 3D Reconstruction Using Multiple Uncalibrated Images , 1995, Int. J. Robotics Res..

[16]  A. Shashua,et al.  Shape tensors for efficient and learnable indexing , 1995, Proceedings IEEE Workshop on Representation of Visual Scenes (In Conjunction with ICCV'95).

[17]  Nassir Navab,et al.  Relative affine structure: theory and application to 3D reconstruction from perspective views , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[18]  P. Anandan,et al.  Parallax Geometry of Pairs of Points for 3D Scene Analysis , 1996, ECCV.

[19]  Ian D. Reid,et al.  Duality, Rigidity and Planar Parallax , 1998, ECCV.

[20]  Paul A. Beardsley,et al.  Active visual navigation using non-metric structure , 1995, Proceedings of IEEE International Conference on Computer Vision.

[21]  Allen R. Hanson,et al.  Description and reconstruction from image trajectories of rotational motion , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[22]  Olivier D. Faugeras,et al.  What can be seen in three dimensions with an uncalibrated stereo rig , 1992, ECCV.

[23]  P. Anandan,et al.  Direct recovery of shape from multiple views: a parallax based approach , 1994, Proceedings of 12th International Conference on Pattern Recognition.