A Comparison of Projective Reconstruction Methods for Pairs of Views

Abstract Recently, different approaches for uncalibrated stereo have been suggested which permit projective reconstructions from multiple views. These use weak calibration which is represented by the epipolar geometry, and so we require no knowledge of the intrinsic or extrinsic camera parameters. In this paper we consider projective reconstructions from pairs of views and compare a number of the available methods. Projective stereo algorithms can be categorized by the way in which the 3D coordinates are computed. The first class is similar to traditional stereo algorithms in that the 3D world geometry is made explicit; the initial phase of the processing always involves the estimation of the camera matrices from which the 3D coordinates are computed. We show how the camera matrices can be computed either from point correspondences, or how they are constrained by the fundamental matrices. The second class of algorithms are based on implicit image measurements which are used to compute projective invariants from image correspondences. The invariants are based on the Cayley algebra and on cross ratios. In all cases, the invariants are functionally dependent on the 3D coordinates. We report on the stabilities of the different methods using a range of meaningful synthetic and real images. From these we can conclude which methods are most likely to be of use in applications that are dependent on 3D uncalibrated reconstructions.

[1]  C. E. Springer,et al.  Geometry and Analysis of Projective Spaces , 1967 .

[2]  Roger Mohr,et al.  Accurate Projective Reconstruction , 1993, Applications of Invariance in Computer Vision.

[3]  Bernd Sturmfels,et al.  Algorithms in invariant theory , 1993, Texts and monographs in symbolic computation.

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

[5]  Tony P. Pridmore,et al.  Geometrical Modeling from Multiple Stereo Views , 1989, Int. J. Robotics Res..

[6]  Lawrence G. Roberts,et al.  Machine Perception of Three-Dimensional Solids , 1963, Outstanding Dissertations in the Computer Sciences.

[7]  Christopher G. Harris,et al.  3D positional integration from image sequences , 1988, Image Vis. Comput..

[8]  Rajiv Gupta,et al.  Stereo from uncalibrated cameras , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  J. Mundy,et al.  Driving vision by topology , 1995, Proceedings of International Symposium on Computer Vision - ISCV.

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

[11]  Patrick Gros,et al.  Outils géométriques pour la modélisation et la reconnaissance d'objets polyédriques. (Geometric tools for modeling and recognizing polyhedral objects) , 1993 .

[12]  Emanuele Trucco,et al.  Geometric Invariance in Computer Vision , 1995 .

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

[14]  Andrew Blake,et al.  Planar region detection and motion recovery , 1993, Image Vis. Comput..

[15]  J. G. Semple,et al.  Algebraic Projective Geometry , 1953 .

[16]  O. Faugeras Strati cation of D vision projective a ne and metric representations , 1995 .

[17]  Olivier D. Faugeras,et al.  Building a Consistent 3D Representation of a Mobile Robot Environment by Combining Multiple Stereo Views , 1987, IJCAI.

[18]  James T. Tippett,et al.  OPTICAL AND ELECTRO-OPTICAL INFORMATION PROCESSING, , 1965 .

[19]  Christopher G. Harris,et al.  3D positional integration from image sequences , 1988, Image Vis. Comput..

[20]  Thierry Viéville,et al.  Canonic Representations for the Geometries of Multiple Projective Views , 1994, ECCV.

[21]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[22]  Olivier D. Faugeras,et al.  Computing three dimensional project invariants from a pair of images using the Grassmann-Cayley algebra , 1998, Image Vis. Comput..

[23]  Olivier D. Faugeras,et al.  A comparison of projective reconstruction methods for pairs of views , 1995, Proceedings of IEEE International Conference on Computer Vision.

[24]  Richard I. Hartley,et al.  Projective Reconstruction and Invariants from Multiple Images , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  S. P. Mudur,et al.  Three-dimensional computer vision: a geometric viewpoint , 1993 .

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

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

[28]  O. D. Faugeras,et al.  Camera Self-Calibration: Theory and Experiments , 1992, ECCV.

[29]  W. Eric L. Grimson,et al.  Computational Experiments with a Feature Based Stereo Algorithm , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[31]  Rachid Deriche,et al.  Robust Recovery of the Epipolar Geometry for an Uncalibrated Stereo Rig , 1994, ECCV.

[32]  A. Venetsanopoulos,et al.  Order statistics in digital image processing , 1992, Proc. IEEE.

[33]  Alamgir Choudhury,et al.  Kinematics of an n-Degree- of-Freedom Multi-Link Robotic System , 1989, Int. J. Robotics Res..

[34]  L. Robert Perception stereoscopique de courbes et de surfaces tridimensionnelles. Application a la robotique mobile , 1993 .

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