Model-Based Recognition of 3D Objects from Single Images

  • Authors:

  • Isaac Weiss;Manjit Ray

  • Affiliations:

  • Univ. of Maryland, College Park;Siemens Medical Systems, Hoffman Estates, IL

  • Venue:
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Year:
  • 2001

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Abstract

In this work, we treat major problems of object recognition which have received relatively little attention lately. Among them are the loss of depth information in the projection from a 3D object to a single 2D image, and the complexity of finding feature correspondences between images. We use geometric invariants to reduce the complexity of these problems. There are no geometric invariants of a projection from 3D to 2D. However, given certain modeling assumptions about the 3D object, such invariants can be found. The modeling assumptions can be either a particular model or a generic assumption about a class of models. Here, we use such assumptions for single-view recognition. We find algebraic relations between the invariants of a 3D model and those of its 2D image under general projective projection. These relations can be described geometrically as invariant models in a 3D invariant space, illuminated by invariant 驴light rays,驴 and projected onto an invariant version of the given image. We apply the method to real images.