mlsanityData to 7 Oct 2026

Know What Your Neighbors Do: 3D Semantic Segmentation of Point Clouds

arXiv 1810.01151 ↗First posted 2 Oct 2018cs.CVcited by 22

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Abstract (arXiv)

In this paper, we present a deep learning architecture which addresses the problem of 3D semantic segmentation of unstructured point clouds. Compared to previous work, we introduce grouping techniques which define point neighborhoods in the initial world space and the learned feature space. Neighborhoods are important as they allow to compute local or global point features depending on the spatial extend of the neighborhood. Additionally, we incorporate dedicated loss functions to further structure the learned point feature space: the pairwise distance loss and the centroid loss. We show how to apply these mechanisms to the task of 3D semantic segmentation of point clouds and report state-of-the-art performance on indoor and outdoor datasets.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Oct 2018.

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Citing papers per year: 2021: 3; 2022: 7; 2023: 4; 2024: 7; 2025: 1.

22 papers.

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  1. 2023OpenMask3D: Open-Vocabulary 3D Instance Segmentationcited by 288
  2. 2021Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based Perceptioncited by 57
  3. 2022STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Datasetcited by 52
  4. 20224D-StOP: Panoptic Segmentation of 4D LiDAR using Spatio-temporal Object Proposal Generation and Aggregationcited by 35
  5. 2021Deep Learning Based 3D Segmentation: A Surveycited by 30
  6. 20223D Segmentation of Humans in Point Clouds with Synthetic Datacited by 24
  7. 2024A Survey on Text-guided 3D Visual Grounding: Elements, Recent Advances, and Future Directionscited by 24
  8. 2023Clustering based Point Cloud Representation Learning for 3D Analysiscited by 23
  9. 2024Multimodality Helps Few-shot 3D Point Cloud Semantic Segmentationcited by 20
  10. 2024Rethinking Few-shot 3D Point Cloud Semantic Segmentationcited by 18
  11. 2023Recent Advances in Multi-modal 3D Intelligence: A Comprehensive Survey and Evaluationcited by 12
  12. 2024Open-Vocabulary 3D Semantic Segmentation with Text-to-Image Diffusion Modelscited by 12
10 more
  1. 2022PST: Plant segmentation transformer for 3D point clouds of rapeseed plants at the podding stagecited by 6
  2. 2022Sequential Point Clouds: A Surveycited by 5
  3. 2023Full Point Encoding for Local Feature Aggregation in 3D Point Cloudscited by 5
  4. 2022Understanding the Challenges When 3D Semantic Segmentation Faces Class Imbalanced and OOD Datacited by 4
  5. 2024Advancements in Point Cloud-Based 3D Defect Detection and Classification for Industrial Systems: A Comprehensive Surveycited by 3
  6. 2025Towards scalable organ level 3D plant segmentation: Bridging the data algorithm computing gapcited by 1
  7. 2021Point Cloud Segmentation Using Sparse Temporal Local Attentioncited by 0
  8. 2022Deep Algebraic Fitting for Multiple Circle Primitives Extraction from Raw Point Cloudscited by 0
  9. 2024Region-Transformer: Self-Attention Region Based Class-Agnostic Point Cloud Segmentationcited by 0
  10. 2024Soft Masked Transformer for Point Cloud Processing with Skip Attention-Based Upsamplingcited by 0