A Global Point-Sift Attention Network for 3D Point Cloud Semantic Segmentation

Abstract

The existing 3D point cloud classification/segmentation networks directly use Convolutional Neural Networks (C-NNs) to extract features from indoor data and have no advantages handling complex outdoor scenes. This is mainly due to the segmentation of large scale outdoor scenes depends on global context information. Inspired by Global Attention (GA) mechanism, we design a Global Point Attention module (GPA) by regarding high-level features, which usually contain rich semantic information, as a guidance to low-level features. In this paper, we embed GPA in PointSIFT to accomplish segmentation and call this new network PointSIFT-GPA. Experimental results on the US3D dataset demonstrate the performance of GPA and the superior performance of PointSIFT-GPA. In particular, PointSIFT-GPA ranks the 2-nd place on 2019 IEEE GRSS Data Fusion Contest 3D Point Cloud Classification Challenge with mIoU 0.9454.