Xiaoqing Ye
Papers
3
Total Citations
86
H-Index
2
About
Xiaoqing Ye is a leading researcher in 3D object detection, a cornerstone technology for autonomous driving and robotics. Ye’s most impactful contribution is the development of **ZoomNet**, a part-aware adaptive zooming neural network for stereo imagery-based 3D detection. This pioneering framework directly tackles the persistent challenges of accurately estimating the 3D pose of distant and occluded objects—a critical bottleneck for real-world perception systems. With over 80 citations, ZoomNet has become a recognized reference point in the field, demonstrating its significant influence on subsequent research. More recently, Ye has advanced the state of the art by exploring temporal feedback mechanisms in 3D detection, as seen in the 2025 work “Coupling and Decoupling: Towards Temporal Feedback for 3D Object Detection.” This line of inquiry leverages sequential data to improve detection robustness over time. Through a career focused on solving fundamental perception problems, Ye has established a reputation for developing novel, adaptive architectures that push the boundaries of what is possible in 3D scene understanding for autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection81 citations · 2020
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