Abstract
The viewpoint consistency constraint requires that the locations of all object features in an image must be consistent with projection from a single viewpoint. The application of this constraint is central to the problem of achieving robust recognition, since it allows the spatial information in an image to be compared with prior knowledge of an object's shape to the full degree of available image resolution. In addition, the constraint greatly reduces the size of the search space during model-based matching by allowing a few initial matches to provide tight constraints for the locations of other model features. Unfortunately, while simple to state, this constraint has seldom been effectively applied in model-based computer vision systems. This paper reviews the history of attempts to make use of the viewpoint consistency constraint and then describes a number of new techniques for applying it to the process of model-based recognition. A method is presented for probabilistically evaluating new potential matches to extend and refine an initial viewpoint estimate. This evaluation allows the model-based verification process to proceed without the expense of backtracking or search. It will be shown that the effective application of the viewpoint consistency constraint, in conjunction with bottom-up image description based upon principles of perceptual organization, can lead to robust three-dimensional object recognition from single gray-scale images.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
R.A. Brooks, “Symbolic reasoning among 3-D models and 2-D images,” Artificial Intelligence, 17, pp. 285–348, 1981.
C. Bundesen and A. Larsen, “Visual transformation of size,” Journal of Experimental Psychology: Human Perception and Performance 1, pp. 214–220, 1975.
M.B. Clowes, “On seeing things,” Artificial Intelligence 2, pp. 79–116, 1971.
S.D. Conte and C.de Boor, Elementary Numerical Analysis: An Algorithmic Approach, 3rd edn, New York: McGraw-Hill, 1980.
L.A. Cooper and R.N. Shepard, “Turning something over in the mind,” Scientific American 251, pp. 106–114, 1984.
R.O. Duda and P.E. Hart, Pattern Classification and Scene Analysis, New York: Wiley, 1973.
O.D. Faugeras, “New steps toward a flexible 3-D vision system for robotics,” in Proceedings of the Seventh International Conference on Pattern Recognition Montreal, 1984, pp. 796–805.
M.A. Fischler and R.C. Bolles, “Random sample consensus: A paradigm formodel fitting with applications to image analysis and automated cartography,” Communications of the ACM 24, pp. 381–395, 1981.
B. Funt, “A parallel-process model of mental rotation,” Cognitive Science 7, pp. 67–93, 1983.
C. Goad, “Special purpose automatic programming for 3D model-based vision,” in Proceedings of the ARPA Image Understanding Workshop, Arlington, Virginia, pp. 94–104, 1983.
E. Grimson and T. Lozano-Pérez, “Model-based recognition and localization from sparse range or tactile data,” International Journal of Robotics Research 3, pp. 3–35, 1984.
A. Guzman, “Decomposition of a visual seene into three-dimensional bodies,” AFIPS Fall Joint Conferences 33, pp. 291–304, 1968.
D.A. Huffman, “Impossible objects as nonsense sentences,” in R. Meltzer and D. Michie (Eds.), Machine Intelligence 6, R. Meltzer and D. Michie (eds.), New York: Elsevier, 1971, pp. 295–323.
T. Kanade, “Recovery of the three-dimensional shape of an object from a single view,” Artificial Intelligence 17, pp. 409–460, 1981.
D.G. Lowe, “Solving for the parameters of object models from image deseriptions,” in Proceedings of the ARPA Image Understanding Workshop, College Park, MD. 1980, pp. 121–127.
D.G. Lowe and T.O. Binford, “Perceptual organization as a basis for visual recognition,” in Proceedings of AAAI-83, Washington, DC, 1983, pp. 255–260.
D.G. Lowe, Perceptual Organization and Visual Recognition, Boston: Kluwer, 1985.
D.G. Lowe, “Three-dimensional object recognition from single two-dimensional images,” Courant Institute Robotics Report, No. 62, New York University, 1986. To appear in Artificial Intelligence.
A.K. Mackworth, “Interpreting pictures of polyhedral scenes,” Artificial Intelligence 1, pp. 121–137, 1973.
D. Marr and E. Hildreth, “Theory of edge detection,” Proceedings of Royal Society of London B 207, pp. 187–217, 1980.
M.J. Morgan, “Mental rotation: A computationally plausible account of transformation through intermediate steps,” Perception 12, pp. 203–211, 1983.
L.G. Roberts, “Machine perception of three-dimensional objects,” in Optical and Electro-optical Information Processing, Tippet et al. (eds.) Cambridge, MA.: MIT Press, 1966, pp. 159–197.
J.T. Schwartz and M. Sharir, “Identification of partially obscured objects in two dimensions by matching of noisy characteristic curves,” Tech. Report 165, Courant Institute, New York University, 1985.
R.N. Shepard and J. Metzler, “Mental rotation of three-dimensional objects,” Science 171, pp. 701–703, 1971.
D. Waltz, “Understanding line drawings of scenes with shadows,” in The Psychology of Computer Vision, P.H. Winston (ed.), New York: McGraw-Hill, 1975.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Lowe, D.G. The viewpoint consistency constraint. Int J Comput Vision 1, 57–72 (1987). https://doi.org/10.1007/BF00128526
Issue Date:
DOI: https://doi.org/10.1007/BF00128526