Dr. Zhao Song| Machine Vision| Best Researcher Award

Associate Researcher,  Hangzhou Innovation Research Institute of Beihang University, China

🔬 Short Biography 🌿💊📚

🎓 Education

Dr. Zhao Song has a solid educational background that reflects his expertise in automation, systems engineering, and artificial intelligence. He earned his Bachelor’s degree in Automation from Shandong University of Science and Technology (2007–2011). He then pursued a Master’s degree in Systems Engineering from Nankai University (2011–2014), 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 (2017–2021), focusing on photometric stereo and 3D reconstruction technologies.

💼 Experience

Dr. Song began his professional career as an Algorithm Engineer at Guangzhou GRG Banking Equipment Co., Ltd. (2014–2016), where he specialized in embedded C programming for ATM systems. He then transitioned to research as an assistant at the Chinese Academy of Sciences (2016–2017). His postdoctoral research at Huawei Technologies Co., Ltd. (2021–2023) 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.

🛠️ 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.

🔬 Research Focus

Dr. Song’s 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.

🏆 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.