Wen Li

Orcid: 0000-0002-4499-9587

Affiliations:
  • Xiamen University, Xiamen, Fujian, China


According to our database1, Wen Li authored at least 13 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2024
Crack-U<sup>2</sup>Net: Multiscale Feature Learning Network for Pavement Crack Detection From Large-Scale MLS Point Clouds.
IEEE Trans. Intell. Transp. Syst., November, 2024

NIDALoc: Neurobiologically Inspired Deep LiDAR Localization.
IEEE Trans. Intell. Transp. Syst., May, 2024

LiSA: LiDAR Localization with Semantic Awareness.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

DiffLoc: Diffusion Model for Outdoor LiDAR Localization.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023
SGLoc: Scene Geometry Encoding for Outdoor LiDAR Localization.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
GCN-Based Pavement Crack Detection Using Mobile LiDAR Point Clouds.
IEEE Trans. Intell. Transp. Syst., 2022

Detection of Individual Trees in UAV LiDAR Point Clouds Using a Deep Learning Framework Based on Multichannel Representation.
IEEE Trans. Geosci. Remote. Sens., 2022

A GCN-Based Method for Extracting Power Lines and Pylons From Airborne LiDAR Data.
IEEE Trans. Geosci. Remote. Sens., 2022

Adaptive Pyramid Context Fusion for Point Cloud Perception.
IEEE Geosci. Remote. Sens. Lett., 2022

Automated extraction of building instances from dual-channel airborne LiDAR point clouds.
Int. J. Appl. Earth Obs. Geoinformation, 2022

WGNet: Wider graph convolution networks for 3D point cloud classification with local dilated connecting and context-aware.
Int. J. Appl. Earth Obs. Geoinformation, 2022

2021
A Local Topological Information Aware Based Deep Learning Method for Ground Filtering from Airborne Lidar Data.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2021

2020
Extraction of Power Lines and Pylons from LiDAR Point Clouds Using a GCN-Based Method.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2020


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