Yanqi Zhou

Papers

Total Citations

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About

Yanqi Zhou is a researcher specializing in computer vision and autonomous robotics perception, with a particular focus on omnidirectional imaging and depth estimation. His most notable contribution, the Omnidirectional Depth Extension Networks (ODE-CNN), addresses a critical challenge in autonomous robot navigation: while 360° cameras have become increasingly accessible and popular for their wide field-of-view advantages, corresponding omnidirectional depth sensors remain prohibitively expensive or technically difficult to deploy. Zhou's work bridges this gap by developing neural network architectures capable of extending depth information to full omnidirectional coverage, significantly enhancing robotic perception systems without requiring costly hardware upgrades. Published in 2020, his research on ODE-CNN has garnered 28 citations, reflecting meaningful engagement from the robotics and computer vision communities. The work tackles a practical bottleneck in autonomous systems — the mismatch between affordable 360° visual sensing and expensive depth sensing — offering a computationally intelligent solution that democratizes omnidirectional depth perception. For students and researchers working at the intersection of deep learning, autonomous navigation, and sensor fusion, Zhou's contributions represent an important step toward more accessible and capable robotic perception systems that leverage the full spatial awareness potential of omnidirectional cameras.