Generating Three-Dimensional Surface Models of Solid Objects from Multiple Projections.

Abstract

A recurring problem in computer vision and related fields is that of generating computer models of physical objects. This thesis presents a method for constructing such models in the form of three-dimensional surface or volume descriptions. The surface models are composed of curved, topologically rectangular, parametric patches. The data required to define these patches are obtained from multiple photographic images of the object illuminated by a rectangular pattern of lines. The projection of the pattern on the surface of the object traces curves which define the boundaries of the patches. The 3D description of the patches is reconstructed by photogrammetric techniques from two or more images of the projected pattern. A calibration stand, in which the object is placed, permits determination of the camera geometry directly from image data. Once the surface descriptions are in the same object space, they are also merged into a common parameter space. This match-and-merge process is iteratively repeated for pairs of surface descriptions until a complete model of the object is assembled.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1982
Accession Number
ADA132392

Entities

People

  • Michael Potmesil

Organizations

  • Rensselaer Polytechnic Institute

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Computational Science
  • Computer Graphics
  • Computer Vision
  • Computer-Aided Design
  • Coordinate Systems
  • Data Processing
  • Digital Images
  • Geometric Forms
  • Geometry
  • Image Processing
  • Lines (Geometry)
  • New York
  • Pattern Recognition
  • Three Dimensional
  • Trees (Data Structures)
  • Two Dimensional

Fields of Study

  • Physics

Readers

  • Computer Vision.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Space
  • Space - Space Objects