Huapeng Su
Papers
1
Total Citations
8
H-Index
1
About
Huapeng Su is a researcher at the forefront of 3D computer vision and autonomous driving perception, with a primary focus on depth completion and sensor fusion. His most cited work, "3dDepthNet: Point Cloud Guided Depth Completion Network for Sparse Depth and Single Color Image" (2020, 8 citations), introduces an end-to-end deep learning architecture that generates accurate dense depth maps from sparse LiDAR data paired with a single RGB image. This innovation directly addresses a critical challenge in robotics and autonomous driving: producing reliable depth information from low-resolution sensors. Su’s key contribution lies in his novel approach to leveraging the dimensional nature of depth images, guiding the network with point cloud priors to achieve superior reconstruction quality. While his citation count is still growing, the practical significance of his work is evident in its potential to enhance real-world perception systems. Su’s research represents an important step toward making autonomous systems safer and more efficient, and his work continues to influence the development of robust 3D scene understanding pipelines.
Research Focus
Key Achievements
Top Papers
- 1