Search Results for author: Jinglun Feng

Found 6 papers, 0 papers with code

Automatic Impact-sounding Acoustic Inspection of Concrete Structure

no code implementations25 Oct 2021 Jinglun Feng, Hua Xiao, Ejup Hoxha, Yifeng Song, Liang Yang, Jizhong Xiao

Impact sounding signal has been shown to contain information about structural integrity flaws and subsurface objects from previous research.

Position Simultaneous Localization and Mapping

Robotic Inspection of Underground Utilities for Construction Survey Using a Ground Penetrating Radar

no code implementations3 Jun 2021 Jinglun Feng, Liang Yang, Ejup Hoxha, Jiang Biao, Jizhong Xiao

Ground Penetrating Radar (GPR) is a very useful non-destructive evaluation (NDE) device for locating and mapping underground assets prior to digging and trenching efforts in construction.

3D Reconstruction GPR +1

Towards 3D Metric GPR Imaging Based on DNN Noise Removal and Dielectric Estimation

no code implementations21 Apr 2021 Jinglun Feng, Liang Yang, Jizhong Xiao

Ground Penetrating Radar (GPR) is one of the most important non-destructive evaluation (NDE) devices to detect subsurface objects (i. e., rebars, utility pipes) and reveal the underground scene.

GPR

GPR-based Model Reconstruction System for Underground Utilities Using GPRNet

no code implementations5 Nov 2020 Jinglun Feng, Liang Yang, Ejup Hoxha, Diar Sanakov, Stanislav Sotnikov, Jizhong Xiao

In this paper, both the quantitative and qualitative experiment results verify our method that can generate a dense and complete point cloud model of pipe-shaped utilities based on a sparse input, i. e., GPR raw data incompleteness and various noise.

3D Reconstruction GPR

GPR-based Subsurface Object Detection and Reconstruction Using Random Motion and DepthNet

no code implementations20 Aug 2020 Jinglun Feng, Liang Yang, HaiYan Wang, Yifeng Song, Jizhong Xiao

This system is composed of three modules: 1) visual inertial fusion (VIF) module to generate the pose information of GPR device, 2) deep neural network module (i. e., DepthNet) which detects B-scan of GPR image, extracts hyperbola features to remove the noise in B-scan data and predicts dielectric to determine the depth of the objects, 3) 3D GPR migration module which synchronizes the pose information with GPR scan data processed by DepthNet to reconstruct and visualize the 3D underground targets.

Depth Estimation Depth Prediction +3

Weakly Supervised Semantic Segmentation in 3D Graph-Structured Point Clouds of Wild Scenes

no code implementations26 Apr 2020 Hai-Yan Wang, Xuejian Rong, Liang Yang, Jinglun Feng, Jizhong Xiao, YingLi Tian

The deficiency of 3D segmentation labels is one of the main obstacles to effective point cloud segmentation, especially for scenes in the wild with varieties of different objects.

3D Semantic Segmentation Point Cloud Segmentation +4

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