Mingkai Yang | Engineering | Best Researcher Award

Mr. Mingkai Yang | Engineering | Best Researcher Award

Dalian Maritime University | China

Mr. Mingkai Yang is a dedicated student at Dalian Maritime University, pursuing his Bachelor of Science degree with a strong focus on computer vision and machine learning. His academic journey reflects a passion for exploring cutting-edge technologies in visual perception and intelligent systems, particularly within the field of Simultaneous Localization and Mapping (SLAM). He has contributed to the research community through completed projects and publications, including the development of MSGS-SLAM, a real-time semantic SLAM system built upon 3D Gaussian Splatting for monocular cameras. This work integrates semantic labels with geometric priors to achieve robust and accurate reconstruction, enabling high-fidelity rendering and providing dense semantic maps for enhanced scene understanding. His research demonstrates state-of-the-art performance in both pose estimation and mapping, reflecting his ability to combine theoretical knowledge with practical innovation. Beyond his independent contributions, he has collaborated with academic mentors, such as Associate Professor Fei Wang, further strengthening his foundation in advanced computational methods and interdisciplinary research. Mingkai also gained exposure to consultancy and industry-related projects, showcasing his ability to apply academic insights to practical challenges. With growing expertise in SLAM and computer vision, he is driven to advance his knowledge and contribute to the broader field of artificial intelligence and robotics, aiming to solve real-world problems through innovative approaches. His commitment to academic excellence, technical innovation, and collaborative research marks him as a promising young scholar with the potential to make significant contributions to science and technology in the coming years.

Profile: ORCID

Featured Publication

Yang, M., Ge, S., & Wang, F. (n.d.). MSGS-SLAM: Monocular Semantic Gaussian Splatting SLAM.