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Structured light 3D scanner simulation dataset.

Michał Własiuk1, Robert Sitnik1

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This study introduces simulated 3D structured light scanner datasets. These datasets enable controlled evaluation of point cloud processing and surface reconstruction algorithms, overcoming real-world data acquisition challenges.

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Area of Science:

  • Computer Vision
  • Optical Metrology
  • Computational Imaging

Background:

  • Real-world optical measurement data acquisition is challenging due to complex setups, environmental sensitivity, noise, and calibration errors.
  • These challenges hinder the controlled evaluation and development of algorithms for 3D data processing.
  • Computer simulation is vital for designing and optimizing optical measurement systems.

Purpose of the Study:

  • To introduce novel datasets generated from a simulated 3D structured light scanner.
  • To provide a controlled environment for algorithm development and evaluation in 3D surface reconstruction and point cloud processing.
  • To mitigate the uncertainties and errors inherent in real-world data acquisition.

Main Methods:

  • Utilized a simulated 3D structured light scanner to project sinusoidal and Gray code patterns onto 3D objects.
  • Simulated various surface materials and controlled illumination conditions within the virtual environment.
  • Generated datasets free from real-world experimental uncertainties and environmental factors.

Main Results:

  • Successfully generated diverse datasets of 3D objects with controlled surface properties and lighting.
  • The simulated data allows for systematic testing and validation of point cloud processing algorithms.
  • The datasets facilitate the development of robust surface reconstruction techniques.

Conclusions:

  • Simulated datasets offer a reliable alternative to real-world data for algorithm development in 3D optical metrology.
  • This approach supports the advancement of point cloud processing and surface reconstruction algorithms under precisely defined conditions.
  • The generated data is valuable for researchers and developers in computer vision and computational imaging.