mlsanityData to 14 Oct 2026

TS40K: a 3D Point Cloud Dataset of Rural Terrain and Electrical Transmission System

arXiv 2405.13989 ↗First posted 22 May 2024cs.CVcited by 1

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Abstract (arXiv)

Research on supervised learning algorithms in 3D scene understanding has risen in prominence and witness great increases in performance across several datasets. The leading force of this research is the problem of autonomous driving followed by indoor scene segmentation. However, openly available 3D data on these tasks mainly focuses on urban scenarios. In this paper, we propose TS40K, a 3D point cloud dataset that encompasses more than 40,000 Km on electrical transmission systems situated in European rural terrain. This is not only a novel problem for the research community that can aid in the high-risk mission of power-grid inspection, but it also offers 3D point clouds with distinct characteristics from those in self-driving and indoor 3D data, such as high point-density and no occlusion. In our dataset, each 3D point is labeled with 1 out of 22 annotated classes. We evaluate the performance of state-of-the-art methods on our dataset concerning 3D semantic segmentation and 3D object detection. Finally, we provide a comprehensive analysis of the results along with key challenges such as using labels that were not originally intended for learning tasks.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 46 of its 65 references that are in the map. First posted on arXiv 22 May 2024.

Builds on

46 of 65 references.

References · 46

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  2. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
  3. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  4. 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,117
  5. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,897
  6. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,451
  7. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,715
  8. 2020Point Transformercited by 1,470
  9. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  10. 20194D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networkscited by 1,208
  11. 2019KPConv: Flexible and Deformable Convolution for Point Cloudscited by 1,205
  12. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 941
34 more
  1. 2019PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detectioncited by 891
  2. 2018PointCNN: Convolution On X-Transformed Pointscited by 764
  3. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 585
  4. 2015Multi-view Convolutional Neural Networks for 3D Shape Recognitioncited by 565
  5. 2019Deep Learning for 3D Point Clouds: A Surveycited by 558
  6. 2019Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Datacited by 532
  7. 2018PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understandingcited by 526
  8. 2020Searching Efficient 3D Architectures with Sparse Point-Voxel Convolutioncited by 423
  9. 2017Joint 2D-3D-Semantic Data for Indoor Scene Understandingcited by 406
  10. 2022Point Transformer V2: Grouped Vector Attention and Partition-based Poolingcited by 388
  11. 2019From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Networkcited by 368
  12. 2020PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentationcited by 275
  13. 2021PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detectioncited by 232
  14. 2020Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completioncited by 223
  15. 2021One Million Scenes for Autonomous Driving: ONCE Datasetcited by 218
  16. 2023Rethinking Range View Representation for LiDAR Segmentationcited by 128
  17. 2023Spherical Transformer for LiDAR-based 3D Recognitioncited by 125
  18. 2022Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentationcited by 119
  19. 20183DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentationcited by 118
  20. 2017Deep Projective 3D Semantic Segmentationcited by 81
  21. 2020Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challengescited by 80
  22. 2020HDNET: Exploiting HD Maps for 3D Object Detectioncited by 78
  23. 2019Multi-view PointNet for 3D Scene Understandingcited by 76
  24. 2016Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networkscited by 72
  25. 2018VV-Net: Voxel VAE Net with Group Convolutions for Point Cloud Segmentationcited by 64
  26. 2021Behind the Curtain: Learning Occluded Shapes for 3D Object Detectioncited by 58
  27. 20163D Fully Convolutional Network for Vehicle Detection in Point Cloudcited by 57
  28. 2018IPOD: Intensive Point-based Object Detector for Point Cloudcited by 46
  29. 2020Cityscapes 3D: Dataset and Benchmark for 9 DoF Vehicle Detectioncited by 39
  30. 2020Learning to Segment 3D Point Clouds in 2D Image Spacecited by 24
  31. 2022GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimationcited by 24
  32. 2018Flex-Convolution (Million-Scale Point-Cloud Learning Beyond Grid-Worlds)cited by 23
  33. 2022An Adjustable Farthest Point Sampling Method for Approximately-sorted Point Cloud Datacited by 14
  34. 2019PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinementcited by 13

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  1. 2025Enhancing Power Grid Inspections with Machine Learningcited by 1