When is it Possible to Identify 3D Objects From Single Images Using Class Constraints?

One approach to recognizing objects seen from arbitrary viewpoint is by extracting invariant properties of the objects from single images. Such properties are found in images of 3D objects only when the objects are constrained to belong to certain classes (e.g., bilaterally symmetric objects). Existing studies that follow this approach propose how to compute invariant representations for a handful of classes of objects. A fundamental question regarding the invariance approach is whether it call be applied to a wide range of classes. To answer this question it is essential to study the set of classes for which invariance exists. This paper introduces a new method for determining the existence of invariance for classes of objects together with the set of images from which these invariance can be computed. We develop algebraic tests that, given a class of objects undergoing affine projection, determine whether the objects in the class can be identified from single images. In addition, these tests allow us to determine the sell of views of the objects which are degenerate. We apply these tests to several classes of objects and determine which of them is identifiable and which of their views are degenerate.

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

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

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

[4]  David A. Forsyth,et al.  Canonical Frames for Planar Object Recognition , 1992, ECCV.

[5]  Long Quan,et al.  Conic Reconstruction and Correspondence From Two Views , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Charlie Rothwell Object Recognition through Invariant Indexing , 1995 .

[7]  D. Jacobs Space Efficient 3D Model Indexing , 1992 .

[8]  Tomaso A. Poggio,et al.  Model-based matching of line drawings by linear combinations of prototypes , 1995, Proceedings of IEEE International Conference on Computer Vision.

[9]  Joseph L. Mundy,et al.  Repeated Structures: Image Correspondence Constraints and 3D Structure Recovery , 1993, Applications of Invariance in Computer Vision.

[10]  David G. Lowe,et al.  Perceptual Organization and Visual Recognition , 2012 .

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

[12]  Akihiro Sugimoto Geometric invariant of noncoplanar lines in a single view , 1994, Proceedings of 12th International Conference on Pattern Recognition.

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

[14]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[15]  Michael Werman,et al.  On View Likelihood and Stability , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  T. Poggio,et al.  Recognition and Structure from one 2D Model View: Observations on Prototypes, Object Classes and Symmetries , 1992 .

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

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

[19]  Yutaka Fukui,et al.  3-D Reconstruction Using Mirror Images Based on a Plane Symmetry Recovering Method , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

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

[21]  Ronen Basri,et al.  Recognition by Linear Combinations of Models , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Ding Mingyue,et al.  The unique solution of projective invariants of six points from four uncalibrated images , 1997 .

[23]  Andrew Zisserman,et al.  Applications of Invariance in Computer Vision , 1993, Lecture Notes in Computer Science.

[24]  Andrew Zisserman,et al.  Extracting structure from an affine view of a 3D point set with one or two bilateral symmetries , 1994, Image Vis. Comput..

[25]  Edward M. Riseman,et al.  The non-existence of general-case view-invariants , 1992 .

[26]  Shimon Ullman,et al.  Limitations of Non Model-Based Recognition Schemes , 1992, ECCV.

[27]  Michael Werman,et al.  The study of 3D-from-2D using elimination , 1995, Proceedings of IEEE International Conference on Computer Vision.

[28]  Gunnar Sparr Depth computations from polyhedral images , 1992, Image Vis. Comput..

[29]  Yehezkel Lamdan,et al.  Geometric Hashing: A General And Efficient Model-based Recognition Scheme , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

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

[31]  Isaac Weiss,et al.  Projective invariants of shapes , 1988, Proceedings CVPR '88: The Computer Society Conference on Computer Vision and Pattern Recognition.