Mingxing Tan
Papers
3
Total Citations
132
H-Index
2
About
Mingxing Tan is a leading researcher in 3D computer vision, with a primary focus on point cloud perception for autonomous driving and robotics. His most significant contribution is the development of SWFormer (Sparse Window Transformer), a groundbreaking architecture that directly addresses the fundamental challenge of sparsity in 3D point clouds. By introducing a scalable sparse window attention mechanism, SWFormer efficiently processes the irregular, sparse nature of LiDAR data, achieving state-of-the-art performance in 3D object detection—a critical capability for safe autonomous navigation. This work has garnered over 126 citations, reflecting its substantial impact on the field. Tan’s research also extends to neural architecture search for 3D perception, as demonstrated in his work on LidarNAS, which unifies and automates the design of efficient point cloud networks. Through these innovations, Tan has advanced the practical deployment of deep learning in real-world 3D sensing systems, making him a notable figure in the intersection of transformer architectures and autonomous driving technology.
Research Focus
Key Achievements
Top Papers
- 1SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds126 citations · 2022
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