mlsanityData to 7 Oct 2026

Underground Mapping and Localization Based on Ground-Penetrating Radar

arXiv 2409.16446 ↗First posted 24 Sep 2024cs.CVcited by 5

Not on the map. Why?

Abstract (arXiv)

3D object reconstruction based on deep neural networks has gained increasing attention in recent years. However, 3D reconstruction of underground objects to generate point cloud maps remains a challenge. Ground Penetrating Radar (GPR) is one of the most powerful and extensively used tools for detecting and locating underground objects such as plant root systems and pipelines, with its cost-effectiveness and continuously evolving technology. This paper introduces a parabolic signal detection network based on deep convolutional neural networks, utilizing B-scan images from GPR sensors. The detected keypoints can aid in accurately fitting parabolic curves used to interpret the original GPR B-scan images as cross-sections of the object model. Additionally, a multi-task point cloud network was designed to perform both point cloud segmentation and completion simultaneously, filling in sparse point cloud maps. For unknown locations, GPR A-scan data can be used to match corresponding A-scan data in the constructed map, pinpointing the position to verify the accuracy of the map construction by the model. Experimental results demonstrate the effectiveness of our method.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 20 of its 40 references that are in the map. First posted on arXiv 24 Sep 2024.

Builds on

20 of 40 references.

References · 20

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,264
  3. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 819
  4. 2018PCN: Point Completion Networkcited by 382
  5. 2016Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescuecited by 368
  6. 2016DeMoN: Depth and Motion Network for Learning Monocular Stereocited by 200
  7. 2017Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputscited by 189
  8. 2020GRNet: Gridding Residual Network for Dense Point Cloud Completioncited by 174
  9. 2018CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAMcited by 151
  10. 2020PF-Net: Point Fractal Network for 3D Point Cloud Completioncited by 146
  11. 2017Learning Efficient Point Cloud Generation for Dense 3D Object Reconstructioncited by 133
  12. 2017SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsiscited by 128
8 more
  1. 2018Multiresolution Tree Networks for 3D Point Cloud Processingcited by 91
  2. 2017Hierarchical Surface Prediction for 3D Object Reconstructioncited by 89
  3. 2023ProxyFormer: Proxy Alignment Assisted Point Cloud Completion with Missing Part Sensitive Transformercited by 43
  4. 2019RayNet: Learning Volumetric 3D Reconstruction with Ray Potentialscited by 26
  5. 2021Rethinking Keypoint Representations: Modeling Keypoints and Poses as Objects for Multi-Person Human Pose Estimationcited by 20
  6. 2020Point Cloud Completion by Learning Shape Priorscited by 14
  7. 2019Depth Extraction from Video Using Non-parametric Samplingcited by 11
  8. 2020GPR-based Subsurface Object Detection and Reconstruction Using Random Motion and DepthNetcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 2.

5 papers.

  1. 2025Adaptive Event Stream Slicing for Open-Vocabulary Event-Based Object Detection via Vision-Language Knowledge Distillationcited by 8
  2. 20253D Plant Root Skeleton Detection and Extractioncited by 5
  3. 2025Automated Genomic Interpretation via Concept Bottleneck Models for Medical Roboticscited by 1
  4. 2026Multimodal Plant Root Phenotyping with Integration of 3D Skeleton Extraction and Language Analysiscited by 0
  5. 2026Semantic Privacy Protection with Utility Preservation for 3D Point Cloudscited by 0