Papers
2
Total Citations
13
H-Index
1
About
Bin Ge is a researcher specializing in computer vision and deep learning, with a particular focus on monocular depth estimation and 3D object detection. His work addresses fundamental challenges in extracting three-dimensional spatial understanding from single-camera imagery — a critical capability for autonomous driving, robotics, and augmented reality applications where deploying multi-sensor systems is often impractical or cost-prohibitive. Ge's most notable contribution, "MonoSAID," introduced a scene-level adaptive instance depth estimation framework for monocular 3D object detection, earning 12 citations since its 2023 publication and demonstrating meaningful traction within the computer vision community. This work advances the field by intelligently adapting depth estimation at both the scene and instance levels, improving detection accuracy without relying on expensive LiDAR or stereo equipment. Building on this foundation, Ge's more recent work "LightNet" reflects a compelling shift toward computational efficiency, proposing a lightweight monocular depth estimation architecture incorporating channel re-alignment optimization — a direction increasingly relevant as researchers seek to deploy vision systems on resource-constrained hardware. Together, these contributions position Ge as an emerging voice in practical, scalable 3D perception research.
Research Focus
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Top Papers
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