Quaternary Categorization Strategy for Reconstructing High-Reflectivity Surface in Structured Light Illumination

Area of Science:

• Metrology
• Optical Engineering
• Surface Science

Background:

• Structured light illumination is crucial for high-speed, precise, non-contact surface defect detection.
• High reflectivity of metal surfaces commonly causes point cloud loss, degrading measurement accuracy.

Purpose of the Study:

• To develop a novel strategy for precise measurement of high-reflectivity surfaces using structured light illumination.
• To address the issue of point cloud loss caused by high surface reflectivity.

Main Methods:

• A quaternary categorization strategy classifying pixels based on phase map characteristics.
• Tailored optimization and reconstruction applied to each pixel category.
• Fusion of point clouds from different pixel types for comprehensive surface reconstruction.

Main Results:

• The proposed strategy effectively reduces measurement errors associated with high reflectivity on metal surfaces.
• Demonstrated enhanced robustness against noise compared to conventional methods.
• Achieved precise point cloud acquisition and reconstruction for challenging surfaces.

Conclusions:

• The quaternary categorization strategy offers a robust solution for accurate 3D measurements of high-reflectivity surfaces.
• This method significantly improves the reliability of structured light systems in industrial inspection applications.
• The approach provides a foundation for advanced non-contact metrology on reflective materials.