Global Context Reasoning for Semantic Segmentation of 3D Point Clouds

Yanni Ma, Yulan Guo, Hao Liu, Yinjie Lei, Gongjian Wen
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
more » ... channel independencies. Specifically, channel maps are first represented as graph nodes and the independencies between nodes are then represented as graph edges. PointGCR is a plug-andplay and end-to-end trainable module. It can easily be integrated into an existing segmentation network and achieves a significant performance improvement. We conduct extensive experiments to evaluate the proposed PointGCR module on both indoor and outdoor datasets. Experimental results show that our PointGCR module efficiently captures global contextual dependencies and significantly improve the segmentation performance of several existing networks.
doi:10.1109/wacv45572.2020.9093411 dblp:conf/wacv/MaGLLW20 fatcat:jb5sjimndzegnmqpwkqiuew4ta