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Surface Fitting for Quasi Scattered Data from Coordinate Measuring Systems.

Qing Mao1, Shugui Liu2, Sen Wang3

  • 1State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China. maoqing.love@163.com.

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Summary
This summary is machine-generated.

A new resampling method improves Non-uniform rational B-spline (NURBS) surface fitting for quasi-scattered data. This approach enhances accuracy and efficiency in CAD, medical imaging, and cultural heritage applications.

Keywords:
NURBS fittinghierarchical fittingiterative projection optimizationresamplingscattered data

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Area of Science:

  • Computer Graphics
  • Geometric Modeling
  • Data Fitting

Background:

  • Non-uniform rational B-spline (NURBS) surface fitting is crucial for CAD, medical imaging, and cultural heritage.
  • Quasi-scattered data, common from measurement systems, presents challenges due to its irregular distribution.

Purpose of the Study:

  • To develop an effective and efficient method for NURBS surface fitting from quasi-scattered data.
  • To address the limitations of existing methods in handling data redundancy and high computational cost.

Main Methods:

  • A novel three-step approach: NURBS curve fitting per row, curve resampling, and surface fitting.
  • Utilizes iterative projection optimization for parameterization and reduced projection time.
  • Employs a resampling strategy based on parameters, local peaks, and contour curvature.

Main Results:

  • The proposed method demonstrates speed, effectiveness, and robustness across simulations and practical data.
  • Resampling introduces negligible error, validating its feasibility for diverse data scatterness.
  • Iterative projection optimization enhances parameterization and reduces computational load.

Conclusions:

  • The resampling-based NURBS surface fitting method is a viable solution for quasi-scattered data.
  • The approach offers significant improvements in efficiency and accuracy for various applications.
  • The method is robust and minimizes error, making it suitable for real-world data.