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Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs

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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Submitted on : Monday, May 28, 2018 - 11:36:03 AM
Last modification on : Saturday, January 15, 2022 - 3:58:43 AM
Long-term archiving on: : Wednesday, August 29, 2018 - 1:22:06 PM


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  • HAL Id : hal-01801186, version 1


Loic Landrieu, Simonovsky Martin. Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs. 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), Jun 2018, Salt Lake City, United States. ⟨hal-01801186⟩



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