mlsanityData to 7 Oct 2026

Semantic Segmentation of Urban Textured Meshes Through Point Sampling

arXiv 2302.10635 ↗First posted 21 Feb 2023cs.CVcited by 0

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Abstract (arXiv)

Textured meshes are becoming an increasingly popular representation combining the 3D geometry and radiometry of real scenes. However, semantic segmentation algorithms for urban mesh have been little investigated and do not exploit all radiometric information. To address this problem, we adopt an approach consisting in sampling a point cloud from the textured mesh, then using a point cloud semantic segmentation algorithm on this cloud, and finally using the obtained semantic to segment the initial mesh. In this paper, we study the influence of different parameters such as the sampling method, the density of the extracted cloud, the features selected (color, normal, elevation) as well as the number of points used at each training period. Our result outperforms the state-of-the-art on the SUM dataset, earning about 4 points in OA and 18 points in mIoU.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 49 references that are in the map. First posted on arXiv 21 Feb 2023.

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27 of 49 references.

References · 27

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  3. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  4. 2019KPConv: Flexible and Deformable Convolution for Point Cloudscited by 1,202
  5. 20173D Semantic Segmentation with Submanifold Sparse Convolutional Networkscited by 746
  6. 2018PointConv: Deep Convolutional Networks on 3D Point Cloudscited by 656
  7. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 584
  8. 2017Large-scale Point Cloud Semantic Segmentation with Superpoint Graphscited by 384
  9. 2016OctNet: Learning Deep 3D Representations at High Resolutionscited by 340
  10. 2018Generating 3D faces using Convolutional Mesh Autoencoderscited by 267
  11. 2018SO-Net: Self-Organizing Network for Point Cloud Analysiscited by 210
  12. 2018Tangent Convolutions for Dense Prediction in 3Dcited by 173
15 more
  1. 2018SPLATNet: Sparse Lattice Networks for Point Cloud Processingcited by 158
  2. 2019Modeling Point Clouds with Self-Attention and Gumbel Subset Samplingcited by 151
  3. 2019ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statisticscited by 104
  4. 2018Local Spectral Graph Convolution for Point Set Feature Learningcited by 84
  5. 2020Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challengescited by 80
  6. 2019PCAN: 3D Attention Map Learning Using Contextual Information for Point Cloud Based Retrievalcited by 68
  7. 2019A-CNN: Annularly Convolutional Neural Networks on Point Cloudscited by 66
  8. 2017O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysiscited by 47
  9. 2022Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentationcited by 47
  10. 2020Primal-Dual Mesh Convolutional Neural Networkscited by 44
  11. 2019LatticeNet: Fast Point Cloud Segmentation Using Permutohedral Latticescited by 38
  12. 2016FPNN: Field Probing Neural Networks for 3D Datacited by 25
  13. 2020Torch-Points3D: A Modular Multi-Task Frameworkfor Reproducible Deep Learning on 3D Point Cloudscited by 23
  14. 2021SUM: A Benchmark Dataset of Semantic Urban Meshescited by 23
  15. 2021Juggling With Representations: On the Information Transfer Between Imagery, Point Clouds, and Meshes for Multi-Modal Semanticscited by 1

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