Xu Cheng

Huazhong University of Science and Technology, Nanjing University of Information Science and Technology

Papers

Total Citations

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About

Xu Cheng is a researcher whose work spans the intersection of optical metrology, 3D sensing technologies, and machine learning applications. His most recognized contribution lies in advancing phase-shifting profilometry for industrial 3D measurement, particularly addressing one of the field's most persistent practical challenges: environmental vibration. His 2019 paper on vibration detection and motion compensation for multi-frequency phase-shifting-based 3D sensors has garnered 17 citations, demonstrating meaningful uptake within the metrology and computer vision communities. In this work, Cheng tackled the critical problem of measurement errors induced by unavoidable environmental vibrations in in situ industrial inspection scenarios, offering solutions that make phase-shifting profilometry more robust and reliable for real-world deployment. Beyond optical sensing, Cheng has also explored the frontier of recurrent neural network architectures, contributing a comparative analysis of neural circuit policies (NCP) against established RNN models on sequence data tasks, reflecting a broader interest in evaluating emerging deep learning methods. Together, these contributions position Cheng as a researcher bridging precision measurement engineering and data-driven computational approaches, with demonstrated impact in industrial metrology and growing engagement with modern neural network research.