Researcher, Shenyang Institute of Automation and Chinese Academy of Sciences, China
Dr. Xia Renbo is a distinguished researcher and doctoral supervisor at the Shenyang Institute of Automation, Chinese Academy of Sciences (CAS) 🧠🤖. With a Ph.D. in Engineering from CAS and degrees from Harbin Institute of Technology 🎓, Dr. Xia specializes in industrial optical measurement, robotic vision, and intelligent manufacturing 🔬📸. He has led innovative projects in 3D reconstruction, machine learning, and pattern recognition 🛠️💡. A key contributor to smart industry technologies, he earned recognition with the Liaoning Provincial Science and Technology Progress Award 🏅. His work bridges advanced computer vision and real-world automation challenges .
Profile
🔹 Education & Experience :
Dr. Xia Renbo earned his 🎓 Ph.D. in Engineering in 2006 from the Shenyang Institute of Automation, Chinese Academy of Sciences (CAS), where he specialized in 3D reconstruction for industrial applications. He also holds an 🎓 M.S. (2002) and 🎓 B.S. (2000) in Mechanical Engineering and Automation from Harbin Institute of Technology. His professional journey began as an 👨🔬 Assistant Researcher (2006–2008) at SIA, CAS, where he developed algorithms for photogrammetry and surface reconstruction. He then served as an 👨🔬 Associate Researcher (2009–2018), focusing on 3D vision, defect detection, and camera calibration. Since 2019, he has been a leading 👨🔬 Researcher at SIA, driving projects in intelligent optical measurement and robotic vision systems.
📚 Professional Development :
Dr. Xia Renbo has steadily advanced his career in industrial automation and intelligent systems 🔧🤖. Beginning as an Assistant Researcher, he contributed to early developments in 3D surface reconstruction and photogrammetry 📐📷. As an Associate Researcher, he expanded into multi-camera calibration and defect detection, contributing to industry-grade systems for quality assurance and control 🛠️🧪. Now a lead Researcher, he spearheads high-impact projects in intelligent measurement and robotic vision, applying computer vision and AI to automation tasks 🤖🔍. His leadership reflects a commitment to integrating smart technologies into real-world industrial environments ⚙️🌐.
🔬 Research Focus :
Dr. Xia’s research spans several interconnected domains at the intersection of automation and intelligence 🧠⚙️. He focuses on industrial optical measurement, advancing precision in manufacturing with 3D reconstruction and dynamic tracking technologies 📏🔬. His work in robotic vision and intelligent manufacturing leverages machine learning, computer vision, and pattern recognition to improve industrial adaptability and efficiency 🤖📸. By merging hardware integration with software intelligence, he contributes to the evolution of Industry 4.0 applications 🚀🏭. His research enhances robotic equipment with real-time perception and adaptability, fostering smarter production lines and inspection systems 🛠️📊.
🏆 Awards and Honors :
Dr. Xia Renbo was honored with the 🥉 Third Prize of the Liaoning Provincial Science and Technology Progress Award in 2011. This recognition was awarded for his outstanding contribution to the development of a 3D Photogrammetric System designed for accurate railway tanker volume measurement 🔍🚆. The project showcased his expertise in applying advanced optical measurement techniques to solve complex industrial challenges, further establishing his reputation in the field of intelligent manufacturing and robotic vision 🤖📐
Publication Top Notes :
A Spectral-Domain Low-Coherence Method for Measuring Composite Windshield Thickness
Citation:
Tao Zhang, Renbo Xia, Jibin Zhao, Yanyi Sun, Jiajun Wu, ShengPeng Fu, Yueling Chen.
“A Spectral-Domain Low-Coherence Method for Measuring Composite Windshield Thickness.” IEEE Transactions on Instrumentation and Measurement, 2024.
DOI: 10.1109/TIM.2024.3353865
Summary:
This paper presents a spectral-domain low-coherence interferometry method tailored for non-destructive and high-precision thickness measurement of composite windshields. The proposed technique compensates for multi-layer reflections and surface curvatures, enabling accurate measurements across curved, layered glass structures commonly used in automotive windshields. The method demonstrates enhanced reliability and resolution compared to traditional time-domain approaches, making it suitable for quality control in automotive manufacturing.
Robust Correspondences with Saliency for Point Cloud Registration
Citation:
Yinghao Li, Renbo Xia, Jibin Zhao, Junlan Yi, Taiwen Qiu.
“Robust Correspondences with Saliency for Point Cloud Registration.” Proceedings of the 2024 ACM International Conference on Graphics and Interaction, April 26, 2024.
DOI: 10.1145/3671151.3671191
Summary:
The authors propose a saliency-guided framework for robust point cloud registration. By integrating geometric saliency and feature consistency, the approach significantly improves correspondence accuracy, especially in scenes with partial overlap or heavy noise. Experimental results confirm superior performance compared to traditional methods like ICP and FGR, particularly in challenging real-world 3D environments such as indoor mapping and robotic navigation.
Low-Coherence Measurement Methods for Industrial Parts With Large Surface Reflectance Variations
Citation:
Tao Zhang, Renbo Xia, Jibin Zhao, Jiajun Wu, Shengpeng Fu, Yueling Chen, Yanyi Sun.
“Low-Coherence Measurement Methods for Industrial Parts With Large Surface Reflectance Variations.” IEEE Transactions on Instrumentation and Measurement, 2023.
DOI: 10.1109/TIM.2023.3301894
Summary:
This study develops a low-coherence interferometric system optimized for measuring the thickness of industrial parts with complex surfaces and high reflectance variability. The methodology integrates reflectance compensation and real-time spectral analysis, enabling high-resolution and repeatable measurements on metal, glass, and composite surfaces. The approach is validated across various industrial use cases including machined parts and reflective coatings.
Research on Optimization of Multi-Camera Placement Based on Environment Model
Citation:
Liming Tao, Renbo Xia, Jibin Zhao, Fangyuan Wang, Shengpeng Fu.
“Research on Optimization of Multi-Camera Placement Based on Environment Model.” Proceedings of the 2023 ACM International Conference on Intelligent Systems and Smart Environments, September 15, 2023.
DOI: 10.1145/3629264.3629288
Summary:
This paper introduces an optimization strategy for multi-camera placement in intelligent monitoring environments. Using a 3D environmental model, the proposed system maximizes surveillance coverage and minimizes blind spots by leveraging visibility analysis and coverage redundancy metrics. The algorithm proves effective in simulation and real-world testing, demonstrating practical value in smart buildings and industrial automation setups.
A High-Accuracy Circular Hole Measurement Method Based on Multi-Camera System
Citation:
Liming Tao, Renbo Xia, Jibin Zhao, Tao Zhang, Yinghao Li, Yueling Chen, Shengpeng Fu.
“A High-Accuracy Circular Hole Measurement Method Based on Multi-Camera System.” Measurement, Volume 205, February 2023, Article 112361.
DOI: 10.1016/j.measurement.2022.112361
Summary:
This work presents a multi-camera 3D reconstruction system for precise circular hole measurements in industrial components. The method employs stereo calibration, edge detection, and ellipse fitting techniques to extract geometric parameters with high accuracy. The system’s performance is validated against traditional single-camera and manual measurement approaches, achieving sub-millimeter precision and improved automation suitability.
Conclusion:
Dr. Xia Renbo exemplifies the qualities of a leading researcher—technical depth, cross-disciplinary innovation, real-world impact, and academic mentorship. His groundbreaking work continues to shape the future of intelligent manufacturing and robotic automation. In light of his achievements and contributions, he is a compelling and deserving recipient of the Best Researcher Award.