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Three-Dimensional Registration for Handheld Profiling Systems Based on Multiple Shot Structured Light.

Shirazi Muhammad Ayaz1, Danish Khan2, Min Young Kim3,4

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This summary is machine-generated.

This study introduces a multi-view registration method for 3D handheld profiling systems using structured light. The approach achieves high accuracy for 3D scanning and object reconstruction.

Keywords:
coarse registrationhandheld 3D scanningmulti-view registrationmultiple shot structured lightregistration refinementvisual navigation

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Area of Science:

  • Computer Vision
  • 3D Reconstruction
  • Metrology

Background:

  • Accurate 3D scanning is crucial for various applications.
  • Existing multi-view registration methods face challenges in precision and efficiency.

Purpose of the Study:

  • To propose a novel multi-view registration approach for 3D handheld profiling systems.
  • To enhance the accuracy and robustness of 3D scanning using structured light.

Main Methods:

  • A multi-view registration strategy combining coarse alignment and Iterative Closest Point (ICP) refinement.
  • Homography-based visual navigation for estimating relative orientation and translation parameters.
  • Evaluation using artificial human skull and paper box models.

Main Results:

  • Achieved mean errors of 9.4 μm (height) and 23 μm (breadth) for paper box reconstruction.
  • Demonstrated a root mean square error of less than 1 mm for registering skull point clouds.
  • Quantitative comparison showed the proposed algorithm's effectiveness against ICP variants.

Conclusions:

  • The proposed multi-view registration method significantly improves 3D scanning accuracy.
  • The system is suitable for precise 3D object profiling and reconstruction.
  • This technique offers a robust solution for handheld 3D scanning applications.