Zihang Lai

Papers

Total Citations

221

H-Index

About

Zihang Lai is a researcher whose work sits at the critical intersection of computer vision, robotics, and efficient on-device AI. His primary research focus is on developing practical, real-time algorithms for 3D scene understanding, with a particular emphasis on stereo depth estimation. Lai’s major contribution is pioneering the concept of "anytime" algorithms for depth perception, which allow a system to produce a usable, albeit coarse, disparity map almost instantly and then iteratively refine it as more computational time becomes available. This breakthrough directly addresses the fundamental trade-off between speed and accuracy in resource-constrained environments like mobile robots and drones. His most-cited work, "Anytime Stereo Image Depth Estimation on Mobile Devices" (2019, 208 citations), has become a key reference in the field, demonstrating how to achieve high-quality depth maps in real time without requiring high-end hardware. By enabling robust spatial awareness on low-power platforms, Lai’s research is paving the way for more responsive and autonomous mobile systems, from household robots to field-deployed unmanned aerial vehicles.