mlsanityData to 7 Oct 2026

Point Cloud Semantic Segmentation using Multi Scale Sparse Convolution Neural Network

arXiv 2205.01550 ↗First posted 3 May 2022cs.CV eess.IVcited by 2

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Abstract (arXiv)

In recent years, with the development of computing resources and LiDAR, point cloud semantic segmentation has attracted many researchers. For the sparsity of point clouds, although there is already a way to deal with sparse convolution, multi-scale features are not considered. In this letter, we propose a feature extraction module based on multi-scale sparse convolution and a feature selection module based on channel attention and build a point cloud segmentation network framework based on this. By introducing multi-scale sparse convolution, the network could capture richer feature information based on convolution kernels with different sizes, improving the segmentation result of point cloud segmentation. Experimental results on Stanford large-scale 3-D Indoor Spaces(S3DIS) dataset and outdoor dataset(SemanticKITTI), demonstrate effectiveness and superiority of the proposed mothod.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 31 of its 44 references that are in the map. First posted on arXiv 3 May 2022.

Builds on

31 of 44 references.

References · 31

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2017Squeeze-and-Excitation Networkscited by 4,763
  3. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  4. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
  5. 2019KPConv: Flexible and Deformable Convolution for Point Cloudscited by 1,202
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  10. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 584
  11. 2019Deep Learning for 3D Point Clouds: A Surveycited by 557
  12. 2017Progressive Neural Architecture Searchcited by 483
19 more
  1. 2020Searching Efficient 3D Architectures with Sparse Point-Voxel Convolutioncited by 422
  2. 2019Selective Kernel Networkscited by 401
  3. 2017Large-scale Point Cloud Semantic Segmentation with Superpoint Graphscited by 384
  4. 2019Point-Voxel CNN for Efficient 3D Deep Learningcited by 343
  5. 2020PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentationcited by 274
  6. 2018SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloudcited by 228
  7. 2021Embracing Single Stride 3D Object Detector with Sparse Transformercited by 193
  8. 2017SEGCloud: Semantic Segmentation of 3D Point Cloudscited by 178
  9. 2018Tangent Convolutions for Dense Prediction in 3Dcited by 173
  10. 2020SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentationcited by 170
  11. 2021(AF)2-S3Net: Attentive Feature Fusion with Adaptive Feature Selection for Sparse Semantic Segmentation Networkcited by 164
  12. 2021Deep Parametric Continuous Convolutional Neural Networkscited by 161
  13. 2019ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statisticscited by 104
  14. 2020AMVNet: Assertion-based Multi-View Fusion Network for LiDAR Semantic Segmentationcited by 87
  15. 2017Deep Projective 3D Semantic Segmentationcited by 81
  16. 20203D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentationcited by 58
  17. 2019SalsaNet: Fast Road and Vehicle Segmentation in LiDAR Point Clouds for Autonomous Drivingcited by 57
  18. 2018Fully-Convolutional Point Networks for Large-Scale Point Cloudscited by 44
  19. 2020TORNADO-Net: mulTiview tOtal vaRiatioN semAntic segmentation with Diamond inceptiOn modulecited by 36

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Cited by year

Citing papers per year: 2023: 2.

2 papers.

  1. 2023Rethinking Range View Representation for LiDAR Segmentationcited by 126
  2. 2023FRNet: Frustum-Range Networks for Scalable LiDAR Segmentationcited by 50