mlsanityData to 14 Oct 2026

LACV-Net: Semantic Segmentation of Large-Scale Point Cloud Scene via Local Adaptive and Comprehensive VLAD

arXiv 2210.05870 ↗First posted 12 Oct 2022cs.CVcited by 5

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Abstract (arXiv)

Large-scale point cloud semantic segmentation is an important task in 3D computer vision, which is widely applied in autonomous driving, robotics, and virtual reality. Current large-scale point cloud semantic segmentation methods usually use down-sampling operations to improve computation efficiency and acquire point clouds with multi-resolution. However, this may cause the problem of missing local information. Meanwhile, it is difficult for networks to capture global information in large-scale distributed contexts. To capture local and global information effectively, we propose an end-to-end deep neural network called LACV-Net for large-scale point cloud semantic segmentation. The proposed network contains three main components: 1) a local adaptive feature augmentation module (LAFA) to adaptively learn the similarity of centroids and neighboring points to augment the local context; 2) a comprehensive VLAD module (C-VLAD) that fuses local features with multi-layer, multi-scale, and multi-resolution to represent a comprehensive global description vector; and 3) an aggregation loss function to effectively optimize the segmentation boundaries by constraining the adaptive weight from the LAFA module. Compared to state-of-the-art networks on several large-scale benchmark datasets, including S3DIS, Toronto3D, and SensatUrban, we demonstrated the effectiveness of the proposed network.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 33 of its 73 references that are in the map. First posted on arXiv 12 Oct 2022.

Builds on

33 of 73 references.

References · 33

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21 more
  1. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 585
  2. 2019DeepGCNs: Can GCNs Go as Deep as CNNs?cited by 527
  3. 2018Simple Baselines for Human Pose Estimation and Trackingcited by 502
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  13. 2020Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadwayscited by 77
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  19. 2021DLA-Net: Learning Dual Local Attention Features for Semantic Segmentation of Large-Scale Building Facade Point Cloudscited by 7
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  21. 2021Background-Aware 3D Point Cloud Segmentationwith Dynamic Point Feature Aggregationcited by 4

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 2; 2025: 1.

5 papers.

  1. 2024Balanced Residual Distillation Learning for 3D Point Cloud Class-Incremental Semantic Segmentationcited by 6
  2. 2024Pathfinder for Low-altitude Aircraft with Binary Neural Networkcited by 4
  3. 2023Small but Mighty: Enhancing 3D Point Clouds Semantic Segmentation with U-Next Frameworkcited by 3
  4. 2025You Only Click Once: Single Point Weakly Supervised 3D Instance Segmentation for Autonomous Drivingcited by 1
  5. 2023APNet: Urban-level Scene Segmentation of Aerial Images and Point Cloudscited by 0