Shengwei Tian

Papers

Total Citations

219

H-Index

About

Shengwei Tian is a leading researcher in computer vision and multimodal artificial intelligence, with a primary focus on 3D point cloud analysis and underwater perception systems. His most impactful contribution is the comprehensive survey "Deep learning-based 3D point cloud classification: A systematic survey and outlook," which has garnered 196 citations, establishing itself as a foundational reference for researchers working on 3D scene understanding and autonomous navigation. Tian has also made significant strides in marine robotics through his work on underwater biological detection, where he enhanced the YOLOv4 detector with channel attention mechanisms to overcome the challenges of turbid water and variable lighting, achieving robust detection of marine species. His recent work introduces MTFR (Modality Transfer and Fusion Refinement), a universal multimodal fusion framework that addresses the critical challenge of integrating heterogeneous data sources—such as LiDAR, RGB, and sonar—for improved perception in complex environments. By bridging the gap between 3D geometric analysis and practical underwater applications, Tian’s research directly impacts autonomous systems, environmental monitoring, and marine biology studies, making him a pivotal figure in advancing robust, real-world computer vision solutions.