Jinglun Feng

Orcid: 0000-0002-2416-7150

According to our database1, Jinglun Feng authored at least 16 papers between 2016 and 2025.

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

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2025
Benchmarking neural radiance fields for autonomous robots: An overview.
Eng. Appl. Artif. Intell., 2025

2024
LTCF-Net: A Transformer-Enhanced Dual-Channel Fourier Framework for Low-Light Image Restoration.
CoRR, 2024

2023
Subsurface Object 3D Modeling Based on Ground Penetration Radar Using Deep Neural Network.
J. Comput. Civ. Eng., November, 2023

Robotic Inspection of Underground Utilities for Construction Survey Using a Ground Penetrating Radar.
J. Comput. Civ. Eng., January, 2023

Robotic Inspection and Subsurface Defect Mapping Using Impact-Echo and Ground Penetrating Radar.
IEEE Robotics Autom. Lett., 2023

Automated wall-climbing robot for concrete construction inspection.
J. Field Robotics, 2023

2022
Robotic Inspection and Characterization of Subsurface Defects on Concrete Structures Using Impact Sounding.
CoRR, 2022

2021
Automatic Impact-sounding Acoustic Inspection of Concrete Structure.
CoRR, 2021

Robotic Inspection and 3D GPR-based Reconstruction for Underground Utilities.
CoRR, 2021

Self-supervised 4D Spatio-temporal Feature Learning via Order Prediction of Sequential Point Cloud Clips.
Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2021

Subsurface Pipes Detection Using DNN-based Back Projection on GPR Data.
Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2021

GPR-based Model Reconstruction System for Underground Utilities Using GPRNet.
Proceedings of the IEEE International Conference on Robotics and Automation, 2021

2020
Weakly Supervised Semantic Segmentation in 3D Graph-Structured Point Clouds of Wild Scenes.
CoRR, 2020

GPR-based Subsurface Object Detection and Reconstruction Using Random Motion and DepthNet.
Proceedings of the 2020 IEEE International Conference on Robotics and Automation, 2020

2016
A multi-robot dynamic formation scheme based on rigid formation.
Proceedings of the IEEE International Conference on Information and Automation, 2016

A strategy of multi-robot formation and obstacle avoidance in unknown environment.
Proceedings of the IEEE International Conference on Information and Automation, 2016


  Loading...