ML Anthology

Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs

Loic Landrieu, Martin Simonovsky
CVPR 2018
doi:10.1109/CVPR.2018.00479 /cvpr/2018/landrieu2018cvpr-largescale/

Abstract

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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Cite

Text

Landrieu and Simonovsky. "Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. doi:10.1109/CVPR.2018.00479

Markdown

[Landrieu and Simonovsky. "Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.](https://mlanthology.org/cvpr/2018/landrieu2018cvpr-largescale/) doi:10.1109/CVPR.2018.00479

BibTeX

@inproceedings{landrieu2018cvpr-largescale,
  title     = {{Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs}},
  author    = {Landrieu, Loic and Simonovsky, Martin},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2018},
  doi       = {10.1109/CVPR.2018.00479},
  url       = {https://mlanthology.org/cvpr/2018/landrieu2018cvpr-largescale/}
}