New algorithm for calculating an invariant of 3D point sets from a single view

The invariant used as an index has shown many advantages over the pose dependent methods in model-based object recognition. Although perspective, or even weak perspective invariants do not exist for general three-dimensional point sets from a single view, invariants do exist for structured three-dimensional point sets. However, such invariants are not easy to derive. A new interpretation of calculating invariants for a special structure of three-dimensional objects is presented. The 3D invariant structure proposed by Rothwell requires seven points that lie on the vertices of a six-sided polyhedral and is applicable to position free objects. In comparison, the proposed algorithm requires only six points on adjacent (virtual) planes that provide two sets of four coplanar points and does not require the position free condition. Hence it is applicable to a wider class of objects. The algorithm is demonstrated on images from real scenes.

[1]  Radu Horaud,et al.  An analytic solution for the perspective 4-point problem , 1989, Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  David W. Jacobs,et al.  Space and Time Bounds on Indexing 3D Models from 2D Images , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  K. Sugihara Machine interpretation of line drawings , 1986, MIT Press series in artificial intelligence.

[4]  Joseph L. Mundy,et al.  Projective geometry for machine vision , 1992 .

[5]  Eamon B. Barrett,et al.  General methods for determining projective invariants in imagery , 1991, CVGIP Image Underst..

[6]  Jake K. Aggarwal,et al.  CAD-based vision: object recognition in cluttered range images using recognition strategies , 1993 .

[7]  Yehezkel Lamdan,et al.  Object recognition by affine invariant matching , 2011, Proceedings CVPR '88: The Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  Long Quan,et al.  Invariants of Six Points and Projective Reconstruction From Three Uncalibrated Images , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Jake K. Aggarwal,et al.  Model-based object recognition in dense-range images—a review , 1993, CSUR.

[10]  Gunnar Sparr,et al.  A Common Framework for Kinetic Depth, Reconstruction and Motion for Deformable Objects , 1994, ECCV.

[11]  David A. Forsyth,et al.  3D Object Recognition Using Invariance , 1995, Artif. Intell..

[12]  Enrico Grosso,et al.  Relative positioning with uncalibrated cameras , 1992 .

[13]  D. W. Thompson,et al.  Three-dimensional model matching from an unconstrained viewpoint , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[14]  David A. Forsyth,et al.  Extracting projective structure from single perspective views of 3D point sets , 1993, 1993 (4th) International Conference on Computer Vision.

[15]  J.B. Burns,et al.  View Variation of Point-Set and Line-Segment Features , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  David A. Forsyth,et al.  Invariant Descriptors for 3D Object Recognition and Pose , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

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

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