Neng Wang

National University of Defense Technology, Shandong University of Technology

Papers

Total Citations

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About

Neng Wang is a researcher working at the intersection of autonomous perception, robotics, and deep learning, with a particular focus on advancing sensor-based environmental understanding for intelligent systems. His work addresses critical challenges in making autonomous vehicles and mobile robots more capable of perceiving and navigating complex real-world environments. Wang's most impactful contribution lies in improving LiDAR and radar-based perception pipelines. His SegNet4D framework introduces an efficient, instance-aware approach to 4D semantic segmentation of LiDAR point clouds, enabling robots and autonomous vehicles to classify objects and detect dynamic elements without the prohibitive computational cost of traditional 4D convolution methods — garnering 16 combined citations across versions. His diffusion-based point cloud super-resolution work tackles the notorious sparsity and ghost-point problems of millimeter-wave radar, enhancing its viability for all-weather perception tasks, accumulating 14 citations since 2024. Beyond perception, Wang has also contributed to robotics motion planning, developing a hybrid Slime Mould Whale Optimization Algorithm for robot joint trajectory planning, improving convergence speed and global search performance. With a growing citation record across cutting-edge robotics and autonomous systems research, Wang represents an emerging voice in intelligent robotic perception.