Papers
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About
Yile Xiao is a rising researcher in the field of optical metrology and computational imaging, with a primary focus on high-speed, high-precision three-dimensional (3D) shape measurement. Their work centers on advancing fringe projection profilometry (FPP) through the integration of deep learning and novel optical techniques. Xiao’s most notable contribution is the development of a deep learning-enhanced method for dynamic 3D shape measurement that employs single-shot spatial multiplexing and a transformer-based phase retrieval network. This approach enables real-time, high-accuracy 3D imaging, addressing critical challenges in industrial inspection, robotic navigation, and human–computer interaction. While their citation count is currently emerging, the innovative fusion of transformer architectures with FPP represents a significant step forward in the field, promising to overcome traditional limitations in speed and precision. Xiao’s work stands out for its forward-looking application of artificial intelligence to solve practical measurement problems, positioning them as a promising contributor to the next generation of intelligent optical systems.
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