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Real-time Rail Recognition Based on 3D Point Clouds

arXiv 2201.02726 ↗First posted 8 Jan 2022cs.CVcited by 0

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Abstract (arXiv)

Accurate rail location is a crucial part in the railway support driving system for safety monitoring. LiDAR can obtain point clouds that carry 3D information for the railway environment, especially in darkness and terrible weather conditions. In this paper, a real-time rail recognition method based on 3D point clouds is proposed to solve the challenges, such as disorderly, uneven density and large volume of the point clouds. A voxel down-sampling method is first presented for density balanced of railway point clouds, and pyramid partition is designed to divide the 3D scanning area into the voxels with different volumes. Then, a feature encoding module is developed to find the nearest neighbor points and to aggregate their local geometric features for the center point. Finally, a multi-scale neural network is proposed to generate the prediction results of each voxel and the rail location. The experiments are conducted under 9 sequences of 3D point cloud data for the railway. The results show that the method has good performance in detecting straight, curved and other complex topologies rails.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 47 references that are in the map. First posted on arXiv 8 Jan 2022.

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15 of 47 references.

References · 15

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2017Mask R-CNNcited by 7,259
  3. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  4. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  5. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  6. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  7. 2019KPConv: Flexible and Deformable Convolution for Point Cloudscited by 1,202
  8. 2019PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detectioncited by 890
  9. 2019CenterNet: Keypoint Triplets for Object Detectioncited by 654
  10. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 584
  11. 2017Semantic3D.net: A new Large-scale Point Cloud Classification Benchmarkcited by 158
  12. 2020Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentationcited by 112
3 more
  1. 2015Sparse 3D convolutional neural networkscited by 71
  2. 2019Adaptive Hierarchical Down-Sampling for Point Cloud Classificationcited by 40
  3. 2020Tensor Low-Rank Reconstruction for Semantic Segmentationcited by 29

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