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Global Context Reasoning for Semantic Segmentation of 3D Point Clouds
2020
2020 IEEE Winter Conference on Applications of Computer Vision (WACV)
Global contextual dependency is important for semantic segmentation of 3D point clouds. However, most existing approaches stack feature extraction layers to enlarge the receptive field to aggregate more contextual information of points along the spatial dimension. In this paper, we propose a Point Global Context Reasoning (PointGCR) module to capture global contextual information along the channel dimension. In PointGCR, an undirected graph representation (namely, ChannelGraph) is used to learn
doi:10.1109/wacv45572.2020.9093411
dblp:conf/wacv/MaGLLW20
fatcat:jb5sjimndzegnmqpwkqiuew4ta