Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs

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...

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Bibliographic Details
Published in2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 4558 - 4567
Main Authors Landrieu, Loic, Simonovsky, Martin
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2018
Subjects
Image edge detection
Image segmentation
Pipelines
Semantics
Shape
Three-dimensional displays
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Summary: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).
ISSN:1063-6919
DOI:10.1109/CVPR.2018.00479