Xinglin Piao

Peng Cheng Laboratory, Beijing University of Technology, Dalian University of Technology

Papers

Total Citations

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About

Xinglin Piao is a researcher advancing the frontiers of 3D computer vision and autonomous systems. His primary research areas include point cloud semantic segmentation, multiagent trajectory prediction, and scene understanding under challenging conditions. Piao’s most notable contribution is his work on point cloud semantic scene segmentation, where he introduced coordinate convolution to address the fundamental challenge of applying convolution operations to irregular and unordered point cloud data. This innovation, published in 2020, has garnered 12 citations and is critical for robotics and autonomous driving applications. In trajectory prediction, Piao developed a global-local scene-enhanced social interaction graph network (2024) that models complex social interactions for safer autonomous navigation. His earlier work on cascaded networks with deep intensity manipulation (2019) tackles scene understanding in low-light environments, directly addressing real-world deployment challenges. With a growing citation impact, Piao’s research bridges foundational geometric deep learning and practical system robustness, making him a rising voice in intelligent perception for autonomous systems.