Deriving object octree from images

  • Jack Veenstra
  • Narendra Ahuja
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 206)


Octrees are used in many 3-D representation problems because they provide a compact data structure, allow rapid access to information, and implement efficient data manipulation algorithms. The initial acquisition of the 3-D information, however, is a common problem. This paper describes an algorithm to construct the octree representation of a 3-D object from silhouette images of the object. The images must be obtained from nine viewing directions corresponding to the three "face-on" and six "edge-on" views of an upright cube. The execution time is found to be linear in the number of nodes in the octree.


Convex Polyhedron Viewing Direction Orthogonal View Silhouette Image Compact Data Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [1]
    N. Ahuja and C. Nash, Octree representations of moving objects, Computer Vision, Graphics, and Image Processing, 26, 1984, 207–216.Google Scholar
  2. [2]
    Rodney Brooks, Symbolic reasoning among models and 2-D images, Artificial Intelligence, 17, (1981) 285–348.CrossRefGoogle Scholar
  3. [3]
    C. H. Chien and J. K. Aggarwal. A volume/surface octree representation, Seventh International Conference on Pattern Recognition, July 30–August 2, 1984.Google Scholar
  4. [4]
    T. H. Hong and M. Shneier, Describing a robot's workspace using a sequence of views from a moving camera, unpublished manuscript, National Bureau of Standards.Google Scholar
  5. [5]
    C. L. Jackins and S. L. Tanimoto, Oct-trees and their use in representing three-dimensional objects, Computer Graphics and Image Processing, 14, 1980, 249–270.Google Scholar
  6. [6]
    B. W. Kernighan and D. M. Ritchie, The C Programming Language, Prentice-Hall, Englewood Cliffs, New Jersey (1978).Google Scholar
  7. [7]
    W. Osse and N. Ahuja, Efficient octree representation of moving objects, Proc. 7th Int. Conf. on Pattern Recognition, Montreal, Canada, July 30–August 2, 1984, 821–823.Google Scholar
  8. [8]
    M. Shneier, E. Kent, and P. Mansbach, Representing workspace and model knowledge for a robot with mobile sensors, Proc. Seventh Int. Conf. on Pattern Recognition, Montreal, Canada. July, 1984, 199–202.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1985

Authors and Affiliations

  • Jack Veenstra
    • 1
  • Narendra Ahuja
    • 1
  1. 1.Coordinated Science LaboratoryUniversity of IllinoisUrbana

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