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Related Experiment Videos

Vision sensor planning for 3-D model acquisition.

S Y Chen1, Y F Li

  • 1Zhejiang University of Technology, Zhejiang, China. sy@ieee.org

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|October 26, 2005
PubMed
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This study introduces a new active vision method for automatically building 3-D object models. It uses trend surface analysis and surface curvature to predict unknown areas and determine optimal viewpoints for complete model acquisition.

Area of Science:

  • Computer Vision
  • Robotics
  • Geometric Modeling

Background:

  • Automatic 3-D model acquisition is crucial for various applications.
  • Existing methods often struggle with unknown object geometries and require manual parameter tuning.
  • Trend surface analysis has been used, but typically with polynomial models.

Purpose of the Study:

  • To propose a novel method for automatic 3-D model acquisition using an active vision system.
  • To develop a new mathematical model for predicting unknown object surface areas.
  • To enable incremental 3-D model building with automated sensing parameter determination.

Main Methods:

  • Active vision system with a movable sensor.
  • Trend surface analysis focusing on regional surface features.

Related Experiment Videos

  • Uniform surface model based on surface curvature analysis.
  • Criterion for determining exploration direction and next view parameters.

    • Main Results:

    • A new mathematical model for predicting unknown object surface areas was developed.
    • Automated determination of sensing parameters for incremental model building.
    • Successful implementation demonstrated the method's validity for 3-D model acquisition.

    Conclusions:

    • The proposed method offers an automated approach to 3-D model acquisition.
    • Analysis of trend surfaces and surface curvatures enhances prediction of unknown areas.
    • The system effectively determines optimal viewpoints for comprehensive 3-D model generation.