Yanfei Su
Papers
1
Total Citations
3
H-Index
1
About
Yanfei Su is a researcher advancing the frontiers of computer vision and 3D scene understanding. Her work focuses on bridging the gap between 2D images and 3D representations, particularly through the development of robust cross-domain descriptors. In her most-cited paper, "Learning Cross-Domain Descriptors for 2D-3D Matching with Hard Triplet Loss and Spatial Transformer Network" (2021), Su introduced a novel approach that leverages hard triplet loss and spatial transformer networks to significantly improve the accuracy of matching 2D image features to 3D point clouds. This contribution is critical for applications in augmented reality, robotics, and autonomous navigation, where precise alignment between visual and spatial data is essential. While her citation count is still growing, the technical depth and practical relevance of her work have already established her as a promising voice in the field. Su’s research exemplifies the innovative use of deep learning to solve fundamental problems in geometric computer vision, offering a foundation for future advancements in cross-modal perception and 3D reconstruction.
Research Focus
Key Achievements
Top Papers
- 1