Yuxuan Xiong

Papers

Total Citations

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About

Yuxuan Xiong’s research spans two distinct and impactful domains: 3D computer vision for robotics and distributed optical fiber sensing. In computer vision, Xiong developed LiftFeat, a novel method for 3D geometry-aware local feature matching that addresses the critical challenge of robust visual feature extraction under drastic lighting changes and in low-texture environments—key for applications like SLAM and visual localization. This work, published in 2025, has already garnered 7 citations, signaling its immediate relevance to advancing autonomous navigation. In parallel, Xiong made significant contributions to optical sensing by proposing a distributed twist sensor using frequency-scanning phase-sensitive optical time-domain reflectometry (φ-OTDR) in a spun fiber. This 2023 paper, also with 7 citations, exploits the unique helical structure of stress rods in spun fibers to enable precise twist measurement, offering a novel approach for structural health monitoring and robotics. Xiong’s ability to bridge geometry-aware machine learning with cutting-edge photonic sensing demonstrates a rare interdisciplinary talent, producing high-impact work that pushes the boundaries of both fields.