Xingrui Yang

National University of Defense Technology, China Aerodynamics Research and Development Center

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About

Xingrui Yang’s research bridges the gap between classical robotic perception and cutting-edge neural scene representation. His early work established a foundation in autonomous navigation, notably through “Stereo vision based traversable region detection for mobile robots using u-v-disparity” (2013, 16 citations), which introduced an efficient stereo-vision method for real-time terrain analysis—a critical capability for mobile robots operating in unstructured environments. This contribution remains a reference point for vision-based traversability mapping. More recently, Yang has been at the forefront of evaluating modern 3D vision techniques for robotics. His highly cited 2024 overview, “Benchmarking Neural Radiance Fields for Autonomous Robots” (accumulating over 18 citations across its versions), provides the first systematic framework for assessing NeRF’s practical utility in robotic perception, reconstruction, and navigation. By translating a computer graphics breakthrough into a robotics context, Yang’s benchmarking work helps the community understand where NeRF excels—and where it falls short—for real-world autonomous systems. His trajectory from classical stereo algorithms to neural rendering benchmarks demonstrates a keen ability to identify and evaluate transformative technologies for the field.