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About

Jinhe Su is a researcher advancing the frontiers of 3D computer vision, autonomous systems, and robotic perception. His work focuses on solving critical challenges in spatial understanding, from object detection and instance segmentation in point clouds to multi-sensor fusion for robust localization. Su’s most cited paper, “3D MSSD: A Multilayer Spatial Structure 3D Object Detection Network for Mobile LiDAR Point Clouds” (2021, 16 citations), addresses a key limitation of the PointPillars model by incorporating semantic spatial structures, significantly improving detection accuracy for autonomous driving and robot vision. He further developed “MTCloud,” a multi-type convolutional linkage network for point cloud instance segmentation, and “NMC3D,” a non-overlapping multi-camera calibration method using sparse 3D maps—essential for unmanned systems. Notably, his work “VID-SLAM” (2024, 4 citations) introduces a tightly coupled RGB-D-inertial SLAM framework that achieves highly accurate 6DOF metric localization in diverse indoor environments, demonstrating his expertise in multi-sensor integration. With a growing citation impact and a portfolio spanning object detection, calibration, and SLAM, Su is making tangible contributions to the reliability and intelligence of autonomous platforms.