RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
Open accessAbstract
We study the problem of efficient semantic segmentation for large-scale 3D point clouds. By relying on expensive sampling techniques or computationally heavy pre/post-processing steps, most existing approaches are only able to be trained and operate over small-scale point clouds. In this paper, we introduce RandLA-Net, an efficient and lightweight neural architecture to directly infer per-point semantics for large-scale point clouds. The key to our approach is to use random point sampling instead of more complex point selection approaches. Although remarkably computation and memory efficient, random sampling can discard key features by chance. To overcome this, we introduce a novel local feature aggregation module to progressively increase the receptive field for each 3D point, thereby effectively preserving geometric details. Extensive experiments show that our RandLA-Net can process 1 million points in a single pass with up to 200x faster than existing approaches. Moreover, our RandLA-Net clearly surpasses state-of-the-art approaches for semantic segmentation on two large-scale benchmarks Semantic3D and SemanticKITTI.
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RandLANet adds a grid-based downsampling step and feature aggregation based on random-centered KD-trees to better handle sparse LiDAR samples.
“An evolution of this technique is developed in RandLANet [31], where an additional grid-based downsampling step is added as preprocessing, together with a feature aggregation based on random-centered KD-trees, to better handle the sparse nature of LiDAR samples.”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [31]
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- Authors
- Hu, Q., Yang, B., Xie, L., Rosa, S., Guo, Y., Wang, Z., Trigoni, N., & Markham, A.
- Affiliations
- University of Oxford (GB)
- Sun Yat-sen University (CN)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 11105-11114
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3012494314
- Cited by
- 2,275
- References
- 80
- Citations by year
- 2026: 287 · 2025: 423 · 2024: 483 · 2023: 433 · 2022: 325 · 2021: 258 · 2020: 63 · 2019: 1 · 2018: 1
- FWCI
- 239.89
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- 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)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61972435)
- Natural Science Foundation of Guangdong Province (2019A1515011271)
- China Scholarship Council
- 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 › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- 3D point cloud semantic segmentation, large-scale point clouds, random point sampling, local feature aggregation, lightweight neural architecture, receptive fields, RandLA-Net, SemanticKITTI
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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