Associate Researcher,Β Hangzhou Innovation Research Institute of Beihang University, China
π¬ Short BiographyΒ πΏππ
Dr. Zhao Song is an Associate Researcher at the Hangzhou Innovation Research Institute of Beihang University, China. His work focuses on Machine Vision, where he has made impactful contributions to intelligent visual systems, image recognition, and deep learning applications in automation and robotics. Dr. Songβs research bridges cutting-edge algorithm development with real-world industrial applications, earning him recognition in both academic and technology innovation spheres. As a dedicated scholar and innovator, he has published in top-tier journals and actively collaborates on interdisciplinary projects that advance machine vision technologies. His outstanding contributions make him a strong candidate for the Best Researcher Award in Machine Vision
Profile
Orcid
π Education
Dr. Zhao Song has a solid educational background that reflects his expertise in automation, systems engineering, and artificial intelligence. He earned his Bachelorβs degree in Automation from Shandong University of Science and Technology (2007β2011). He then pursued a Masterβs degree in Systems Engineering from Nankai University (2011β2014), where he laid the groundwork for his algorithmic and system design skills. His academic journey culminated with a Ph.D. in Pattern Recognition and Intelligent Systems from the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (2017β2021), focusing on photometric stereo and 3D reconstruction technologies.
πΌ Experience
Dr. Song began his professional career as an Algorithm Engineer at Guangzhou GRG Banking Equipment Co., Ltd. (2014β2016), where he specialized in embedded C programming for ATM systems. He then transitioned to research as an assistant at the Chinese Academy of Sciences (2016β2017). His postdoctoral research at Huawei Technologies Co., Ltd. (2021β2023) focused on integrating material modeling with structured light systems for digital human modeling. Since September 2023, he has been serving as a Senior Associate Researcher at the Hangzhou Innovation Research Institute of Beihang University, where he leads projects in structured light, photometric modeling, and digital human generation.
π οΈ Skills
Dr. Song possesses a comprehensive skill set in 3D reconstruction, photometric stereo, structured light systems, and material measurement and modeling. His technical proficiency spans C/C++ programming, GPU parallel computing, OpenCV, and real-time image processing algorithms. He is capable of independently designing, building, and optimizing structured light systems for micrometer-level reconstruction. His interdisciplinary approach combines optics, computer vision, and rendering algorithms, making him adept at solving complex problems in material-aware geometry acquisition.
π¬ Research Focus
Dr. Songβs research revolves around 3D reconstruction and material acquisition, with a core focus on photometric stereo, binary stripe structured light, and integrated geometry-material modeling systems. During his Ph.D., he proposed innovative LED-based photometric stereo techniques and developed micrometer-level reconstruction methods for reflective surfaces. As a postdoc, he introduced a novel fusion framework combining photometric cues with structured light for enhanced accuracy. His recent work includes pioneering the first structured light system capable of outputting complete material maps (diffuse, specular, roughness, normal) and investigating DMA correction techniques to improve reconstruction under varying lighting and material conditions. He also contributed to high-fidelity digital human creation using Lightstage systems and NeRF-based geometry fusion.
π Awards & Achievements
Dr. Song has made significant contributions to both academia and industry. His work has led to multiple high-impact publications in journals like Optics Express, Optics and Lasers in Engineering, and Sensors. He has authored several national patents, including groundbreaking methods for 3D object reconstruction and material-aware geometry optimization. His innovations in integrating structured light with material modeling have been successfully translated into commercial applications, notably in digital human rendering. Recognized for his originality and technical acumen, Dr. Song is a prominent candidate for leading awards in Machine Vision and 3D Imaging Systems.
πPublicationΒ
- Title: A novel calibration method for uniaxial MEMS-based structured light system with linear transition function
Journal: Measurement
DOI: 10.1016/j.measurement.2025.117969
Year: 2025
Authors: Yuping Ye, Gang Zhou, Xiujing Gao, Zhenghui Hu, Yi Chen, Zhao Song, Zhan Song
Citations: Not yet available (published for December 2025βmay not have citations yet)
Title: Micrometer-level 3D measurement techniques in complex scenes based on stripe-structured light and photometric stereo
Journal: Optics Express
DOI: 10.1364/OE.401850
Publication Date: October 26, 2020
Authors: Zhao Song, Zhan Song, Juan Zhao, Feifei Gu
Citations: 43 citations
πconclusion:
Dr. Zhao Song is an excellent candidate for the Best Researcher Award. His proven ability to develop cutting-edge, commercial-ready solutions, along with original research that pushes the frontiers of 3D computer vision and graphics, strongly justifies his nomination. Recognizing him with this award would encourage continued innovation at the intersection of vision, AI, and human digitalization.