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Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
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Comparison of Point Cloud Registration Techniques on Scanned Physical Objects.

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  • 1Robotics & Multibody Mechanics Group, Vrije Universiteit Brussel, Pleinlaan 9, 1050 Brussels, Belgium.

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Summary

This study compares six CAD model registration techniques using real-world scans, not synthetic data. GO-ICP and PointNetLK show high accuracy, but most methods struggle with noisy data, aiding researchers in selecting appropriate registration algorithms.

Keywords:
CAD model alignmentdigital twinspoint cloud datasetspoint cloud registration

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Area of Science:

  • Computer Vision
  • Geometric Computing
  • 3D Reconstruction

Background:

  • CAD model alignment is crucial for various applications.
  • Existing registration algorithm evaluations often use synthetic data, which may not reflect real-world complexities.
  • Real-world scans introduce noise and outliers, challenging registration performance.

Purpose of the Study:

  • To conduct a comparative analysis of six prominent registration techniques for CAD model alignment.
  • To evaluate classical and learning-based methods using real-world scan data.
  • To provide quantitative metrics for method selection.

Main Methods:

  • Utilized point clouds from the Cranfield benchmark, including real-world scans of 3D-printed objects.
  • Assessed three classical methods (GO-ICP, RANSAC, FGR) and three learning-based methods (PointNetLK, RPMNet, ROPNet).
  • Evaluated performance using metrics like recall, accuracy, and computation time.

Main Results:

  • GO-ICP, PointNetLK, RANSAC, and RPMNet (with ICP refinement) demonstrated high accuracy.
  • Most methods, except GO-ICP, exhibited failure cases with increased noise or larger transformations.
  • FGR and RANSAC were the fastest, while GO-ICP required several seconds.

Conclusions:

  • Learning-based methods show promise but face training and generalization challenges.
  • GO-ICP and PointNetLK are accurate but GO-ICP is slower.
  • The study provides valuable quantitative insights for selecting registration methods for real-world applications.