点云
计算机科学
分割
人工智能
卷积神经网络
图形
代表(政治)
比例(比率)
图像分割
模式识别(心理学)
计算机视觉
理论计算机科学
地图学
地理
政治学
政治
法学
作者
Loïc Landrieu,Martin Simonovsky
标识
DOI:10.1109/cvpr.2018.00479
摘要
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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