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

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 cont...

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Bibliographic Details
Main Authors: Jia, Meixia, Li, Aijin, Wu, Zhaoyang
Format: Conference Proceeding
Language:English
Subjects:
3D point cloud classification/segmentation
Buildings
Convolutional neural networks
convolutional neural networks (CNNs)
Data integration
Feature extraction
global attention
PointSIFT
Semantics
Three-dimensional displays
Training data
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Description
Summary: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.
ISSN:2153-7003
DOI:10.1109/IGARSS.2019.8900102