Kai Sheng Cheng
Papers
1
Total Citations
6
H-Index
1
About
Kai Sheng Cheng is a computer vision researcher whose work focuses on bridging local and global representations for 3D object recognition. In his most cited paper, "A 3D Recognition System with Local-Global Collaboration" (2017), Cheng introduced a novel framework that synergizes fine-grained local features with holistic global context, enabling robust recognition of 3D objects under varying poses and occlusions. This contribution addresses a critical challenge in the field, improving accuracy and reliability in applications from robotics to augmented reality. With 6 citations, his work has laid early groundwork for subsequent advances in 3D perception, demonstrating a clear understanding of how to balance detail with structure. Cheng’s research is particularly notable for its practical orientation, aiming to make 3D recognition systems more adaptable and efficient for real-world deployment. As a researcher, he continues to explore the intersection of geometric deep learning and multi-modal data, promising further innovations in spatial understanding.
Research Focus
Key Achievements
Top Papers
- 1A 3D Recognition System with Local-Global Collaboration6 citations · 2017