Papers
3
Total Citations
66
H-Index
2
About
Zhiye Wang is a leading researcher in 3D sensing and autonomous robotics, with a primary focus on LiDAR-based perception, super-resolution, and collaborative mapping. His work addresses critical challenges in point cloud processing for both indoor and outdoor environments. Wang’s most cited paper, "SGSR-Net: Structure Semantics Guided LiDAR Super-Resolution Network for Indoor LiDAR SLAM" (2023, 32 citations), introduces a novel deep learning framework that enhances the resolution of multi-beam LiDAR data, significantly improving the accuracy of indoor SLAM systems. He also developed "DCPLD-Net" (2022, 32 citations), a diffusion-coupled convolutional neural network for real-time detection of power transmission lines from UAV-borne LiDAR data, advancing infrastructure inspection and remote sensing. His recent work, "ATCM: Aerial–Terrestrial LiDAR-Based Collaborative Simultaneous Localization and Mapping" (2025), pioneers heterogeneous multi-robot C-SLAM, enabling seamless data fusion between aerial and ground robots for precise global scene reconstruction. With over 66 citations across his key publications, Wang’s contributions are shaping the future of autonomous navigation, robotic perception, and intelligent infrastructure monitoring.
Research Focus
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Top Papers
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