Papers
18
Total Citations
853
H-Index
10
About
Bisheng Yang is a leading researcher in 3D geospatial data processing, LiDAR technology, and robotic perception. His work centers on point cloud registration, semantic scene understanding, and sensor fusion for autonomous systems. Yang’s most impactful contribution is his comprehensive review and benchmark on large-scale terrestrial laser scanner point cloud registration (410 citations), which has become a foundational reference in the field. He pioneered hierarchical methods for extracting urban objects from mobile laser scanning data (221 citations), significantly advancing automated mapping. His recent innovations include SE-Calib, a semantic edge-based method for online LiDAR-camera calibration in urban scenes, and SGSR-Net, a structure-semantics guided super-resolution network that enhances indoor LiDAR SLAM performance. Yang has also developed deep learning solutions for real-time power line detection from UAV-borne LiDAR data and Mobile-Seed for joint semantic segmentation and boundary detection on mobile robots. His work on CoFiI2P addresses the challenging problem of coarse-to-fine image-to-point cloud registration, while DALI-SLAM introduces degeneracy-aware LiDAR-inertial SLAM with robust distortion correction. Through these contributions, Yang has shaped modern 3D perception pipelines for autonomous navigation, infrastructure inspection, and urban digital twins.
Research Focus
Key Achievements
Top Papers
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- 2Hierarchical extraction of urban objects from mobile laser scanning data221 citations · 2014
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