Value probability analysis for linear phase estimation in sinusoidal structured-light range imaging

Area of Science:

• Optics and Photonics
• Computer Vision
• Metrology

Background:

• Accurate 3D reconstruction using sinusoidal structured-light (SL) systems relies heavily on precise linear phase estimation.
• Existing methods often face challenges due to noise and non-linearities affecting phase unwrapping and accuracy.

Purpose of the Study:

• To introduce a novel value probability analysis for enhancing linear phase estimation in SL 3D imaging.
• To demonstrate that optimizing phase probability offers superior performance compared to adjusting intensity or intermediate values.

Main Methods:

• Developed a value probability analysis based on the derivative of continuously differentiable functions.
• Applied this analysis to intensity, phase, and intermediate values in sinusoidal SL.
• Validated the phase probability adjustment technique using both synthetic and real-world SL data.

Main Results:

• Established a direct relationship between a function's value probability and its derivative.
• Showed that phase probability adjustment significantly improves linear phase estimation accuracy.
• Demonstrated the practical effectiveness of the proposed method in diverse SL imaging scenarios.

Conclusions:

• Value probability analysis provides a powerful framework for optimizing SL 3D imaging.
• Phase probability adjustment is a key technique for achieving high-accuracy linear phase estimation.
• The presented method offers a robust solution for improving 3D range imaging with sinusoidal structured light.