Papers
8
Total Citations
436
H-Index
6
About
Wei Feng is a researcher whose work sits at the intersection of computer vision, robotics, and deep learning, with particular expertise in depth perception, autonomous navigation, and camera localization. His most influential contribution, "Learning Guided Convolutional Network for Depth Completion" (2020, 273 citations), tackled a fundamental challenge in autonomous driving by developing a deep learning framework that transforms sparse LiDAR measurements into dense depth maps using synchronized RGB imagery — a breakthrough that has become widely referenced across the robotics and self-driving communities. His 2019 work on map prediction for mobile robot exploration (101 citations) further demonstrated his ability to leverage learned approaches to surpass traditional geometric methods in autonomous indoor navigation. Feng's research portfolio also reflects a sustained interest in active camera relocalization, hand-eye calibration, and human-robot interaction, including early contributions in RGB-D-based hand posture recognition. His 2018 paper on active relocalization from a single reference image introduced a novel, calibration-free paradigm that challenged conventional assumptions in the field. More recently, his foray into material intelligence signals an expanding interdisciplinary vision. Collectively, Feng's body of work — spanning over 400 citations — reflects a researcher continuously pushing the boundaries of intelligent perception and autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Learning Guided Convolutional Network for Depth Completion273 citations · 2020
- 2Learned Map Prediction for Enhanced Mobile Robot Exploration101 citations · 2019
- 3
- 4Fast and Reliable Computational Rephotography on Mobile Device15 citations · 2018
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- 7Dynamic Hybrid Approaching for Robust Hand-Eye Calibration2 citations · 2018
- 8