mlsanityData to 7 Oct 2026

Efficient Urban-scale Point Clouds Segmentation with BEV Projection

arXiv 2109.09074 ↗First posted 19 Sep 2021cs.CVcited by 6

Not on the map. Why?

Abstract (arXiv)

Point clouds analysis has grasped researchers' eyes in recent years, while 3D semantic segmentation remains a problem. Most deep point clouds models directly conduct learning on 3D point clouds, which will suffer from the severe sparsity and extreme data processing load in urban-scale data. To tackle the challenge, we propose to transfer the 3D point clouds to dense bird's-eye-view projection. In this case, the segmentation task is simplified because of class unbalance reduction and the feasibility of leveraging various 2D segmentation methods. We further design an attention-based fusion network that can conduct multi-modal learning on the projected images. Finally, the 2D out are remapped to generate 3D semantic segmentation results. To demonstrate the benefits of our method, we conduct various experiments on the SensatUrban dataset, in which our model presents competitive evaluation results (61.17% mIoU and 91.37% OverallAccuracy). We hope our work can inspire further exploration in point cloud analysis.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 24 of its 30 references that are in the map. First posted on arXiv 19 Sep 2021.

Builds on

24 of 30 references.

References · 24

  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. 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,088
  4. 2019KPConv: Flexible and Deformable Convolution for Point Cloudscited by 1,202
  5. 2018PointCNN: Convolution On X-Transformed Pointscited by 764
  6. 20173D Semantic Segmentation with Submanifold Sparse Convolutional Networkscited by 746
  7. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 584
  8. 2019Deep Learning for 3D Point Clouds: A Surveycited by 557
  9. 2017Joint 2D-3D-Semantic Data for Indoor Scene Understandingcited by 404
  10. 2017Large-scale Point Cloud Semantic Segmentation with Superpoint Graphscited by 384
  11. 2019Point-Voxel CNN for Efficient 3D Deep Learningcited by 343
  12. 2017SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloudcited by 231
12 more
  1. 2018SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloudcited by 228
  2. 2017SEGCloud: Semantic Segmentation of 3D Point Cloudscited by 178
  3. 2018Tangent Convolutions for Dense Prediction in 3Dcited by 173
  4. 2020SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentationcited by 170
  5. 2018SPLATNet: Sparse Lattice Networks for Point Cloud Processingcited by 158
  6. 2017Deep Projective 3D Semantic Segmentationcited by 81
  7. 2020Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challengescited by 80
  8. 2021Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusioncited by 71
  9. 2019LatticeNet: Fast Point Cloud Segmentation Using Permutohedral Latticescited by 38
  10. 2020Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scenecited by 27
  11. 2020Semantic Segmentation on Swiss3DCities: A Benchmark Study on Aerial Photogrammetric 3D Pointcloud Datasetcited by 22
  12. 2020FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware Modellingcited by 17

Built on by

Cited by year

Citing papers per year: 2023: 3; 2024: 3.

6 papers.

  1. 2023MarS3D: A Plug-and-Play Motion-Aware Model for Semantic Segmentation on Multi-Scan 3D Point Cloudscited by 19
  2. 2024Deep Learning on 3D Semantic Segmentation: A Detailed Reviewcited by 6
  3. 2024Pathfinder for Low-altitude Aircraft with Binary Neural Networkcited by 4
  4. 2023Informative Data Selection with Uncertainty for Multi-modal Object Detectioncited by 2
  5. 2023HRHD-HK: A benchmark dataset of high-rise and high-density urban scenes for 3D semantic segmentation of photogrammetric point cloudscited by 2
  6. 2024FASTC: A Fast Attentional Framework for Semantic Traversability Classification Using Point Cloudcited by 0