Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs
Open accessAbstract
We propose a novel deep learning-based framework to tackle the challenge of semantic segmentation of large-scale point clouds of millions of points. We argue that the organization of 3D point clouds can be efficiently captured by a structure called superpoint graph (SPG), derived from a partition of the scanned scene into geometrically homogeneous elements. SPGs offer a compact yet rich representation of contextual relationships between object parts, which is then exploited by a graph convolutional network. Our framework sets a new state of the art for segmenting outdoor LiDAR scans (+11.9 and +8.8 mIoU points for both Semantic3D test sets), as well as indoor scans (+12.4 mIoU points for the S3DIS dataset).
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- Authors
- Landrieu, L., & Simonovsky, M.
- Affiliations
- École nationale des ponts et chaussées (FR)
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 4558-4567
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2769473888
- Cited by
- 1,543
- References
- 56
- Citations by year
- 2026: 99 · 2025: 151 · 2024: 246 · 2023: 236 · 2022: 236 · 2021: 245 · 2020: 201 · 2019: 113 · 2018: 15
- FWCI
- 209.45
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, other-oa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Keywords
- point cloud semantic segmentation, graph convolutional networks, large-scale point clouds, LiDAR scanning, outdoor point clouds, indoor point clouds
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