Projective Structure from Two Uncalibrated Images: Structure from Motion and Recognition

Abstract

This paper addresses the problem of recovering relative structure, in the form of an invariant, from two views of a 3D scene. The invariant structure is computed without any prior knowledge of camera geometry or internal calibration, and with the property that perspective and orthographic projections are treated alike, namely, the system makes no assumption regarding the existence of perspective distortions in the input images. The authors show that, given the location of epipoles, the projective structure invariant can be constructed from only four corresponding points projected from four non-coplanar points in space (like in the case of parallel projection). This result leads to two algorithms for computing projective structure. The first algorithm requires six corresponding points, four of which are assumed to be projected from four coplanar points in space. Alternatively, the second algorithm requires eight corresponding points, without assumptions of coplanarity of object points. Their study of projective structure is applicable to both structure from motion and visual recognition. They use projective structure to re-project the 3D scene from two model images and six or eight corresponding points with a novel view of the scene. The re-projection process is well-defined under all cases of central projection, including the case of parallel projection.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1992
Accession Number
ADA260102

Entities

People

  • Amnon Shashua

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Artificial Intelligence
  • Calibration
  • Computer Vision
  • Coordinate Systems
  • Distortion
  • Equations
  • Geometry
  • Image Processing
  • Image Recognition
  • Object Recognition
  • Projective Geometry
  • Recognition
  • Shape
  • Three Dimensional

Readers

  • Computer Vision.
  • Graph Algorithms and Convex Optimization.

Technology Areas

  • Space
  • Space - Space Objects