Shilei Wen

Papers

Total Citations

H-Index

About

Shilei Wen is a leading researcher in computer vision, with a primary focus on 3D object detection for autonomous driving and robotics. His most significant contribution is the development of **ZoomNet**, a pioneering framework for stereo imagery-based 3D detection. This work directly tackles the persistent challenges of accurately estimating the 3D pose of distant and occluded objects—a critical bottleneck in real-world autonomous systems. By introducing a part-aware adaptive zooming neural network, Wen’s approach enables the model to dynamically focus on informative regions, dramatically improving detection precision for hard-to-see objects. The core paper on ZoomNet has garnered **81 citations**, underscoring its influence and adoption as a key reference in the field. This work stands out for its practical impact, bridging the gap between theoretical advances and the stringent requirements of real-time, safety-critical applications. Wen’s research continues to push the boundaries of what is possible in 3D perception, making him a notable figure for students and researchers seeking to understand state-of-the-art techniques in autonomous perception systems.