Jiaqi Gu
Papers
1
Total Citations
57
H-Index
1
About
Jiaqi Gu is a leading researcher in computer vision and 3D scene understanding, with a primary focus on depth completion and LiDAR-based perception. His most cited work, "DenseLiDAR: A Real-Time Pseudo Dense Depth Guided Depth Completion Network" (2021, 57 citations), tackles the fundamental challenge of producing dense depth maps from sparse LiDAR inputs—a critical problem for autonomous driving and robotics. Gu's key contribution lies in developing a pseudo-dense depth guidance strategy that effectively addresses the inherent sparsity of both input data and ground truth, enabling more complete and accurate 3D environmental descriptions in real time. This work has been widely recognized for its practical impact, bridging the gap between sparse sensor data and the dense depth information required for reliable perception systems. Beyond this flagship paper, Gu's research continues to advance efficient neural architectures for depth estimation and multi-modal sensor fusion. His innovative approach to depth completion has made him a notable figure in the field, with his methods being adopted by both academic researchers and industry practitioners seeking robust solutions for real-world 3D vision tasks.
Research Focus
Key Achievements
Top Papers
- 1DenseLiDAR: A Real-Time Pseudo Dense Depth Guided Depth Completion Network57 citations · 2021