Papers
2
Total Citations
10
H-Index
2
About
Lei Deng is a researcher advancing the fields of 3D vision, robotics, and intelligent perception, with a focus on enabling precise and reliable robotic manipulation. His work bridges the gap between geometric reconstruction and autonomous grasping, particularly in industrial settings. A key contribution is his development of a circular marker-aided multi-view laser point cloud registration method, which employs an adaptive-weighted bundle adjustment technique to achieve highly accurate 3D scene alignment—a critical capability for large-scale metrology and robotics. This work has garnered 6 citations since 2024. In robotics, Deng tackled the challenge of suction-based grasping by proposing a novel RGB-D instance segmentation framework that directly predicts optimal suction points on object surfaces. By moving beyond traditional two-stage decoupled approaches, his method enhances the stability and reliability of industrial pick-and-place operations for objects of varying shapes, earning 4 citations since 2022. Through these contributions, Lei Deng is shaping the future of automated perception and manipulation, providing robust solutions that integrate computer vision, point cloud processing, and robotic control for real-world applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2RGB-D Instance Segmentation-based Suction Point Detection for Grasping4 citations · 2022