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About

Xingshuai Dong is a computer vision and robotics researcher whose work centers on monocular depth estimation (MDE), autonomous driving, and efficient neural network design. His most significant contribution is a comprehensive survey on real-time monocular depth estimation for robotics (2022), which has garnered over 160 citations and stands as a key reference for researchers navigating the rapidly evolving landscape of depth perception techniques. This work systematically examines MDE methods critical to applications such as ego-motion estimation, obstacle avoidance, and scene understanding. Beyond surveying the field, Dong has made meaningful practical contributions through the development of lightweight, deployable architectures. His MobileXNet framework (27 citations) addresses a critical gap between state-of-the-art accuracy and real-world computational constraints, proposing an efficient convolutional neural network suited for resource-limited robotic platforms. His edge-guided network for monocular depth estimation further advances this agenda by leveraging structural edge information to improve depth accuracy without sacrificing efficiency. Earlier work on monocular visual-IMU odometry demonstrates a consistent interest in robust, sensor-efficient robot navigation. Collectively, Dong's research bridges theoretical depth estimation advances with the practical demands of real-time robotics deployment, making him a notable contributor to the field of embodied visual perception.