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About

Xin Kong is a robotics and computer vision researcher whose work spans 3D point cloud processing, autonomous navigation, and human-robot interaction. With a career bridging foundational perception challenges and cutting-edge deep learning applications, Kong has made significant contributions to the fields of LiDAR-based scene understanding and intelligent robotic systems. Among Kong's most recognized contributions is RINet (2022, 67 citations), a rotation-invariant neural network that advances LiDAR-based place recognition — a critical capability enabling robots to reliably identify previously visited locations despite varying viewpoints. Complementing this, his work on semantic segmentation-assisted scene completion (2021, 43 citations) tackles the inherent sparsity of LiDAR data to improve 3D environmental understanding for autonomous driving and robotics. His earlier research on depth-perception-based gesture recognition (2012, 50 citations) demonstrated a prescient interest in natural human-robot interaction under real-world conditions. Kong has also advanced quadruped robot navigation in confined spaces (2021, 40 citations) and introduced the DA² dataset (2022), the first large-scale benchmark for dexterous dual-arm grasping. His lightweight segmentation framework, LessNet, further reflects a consistent commitment to efficiency in real-world deployments. Collectively, his work has garnered over 200 citations, establishing him as a notable voice in intelligent robotics and 3D perception research.