GA-NET: Global Attention Network for Point Cloud Semantic Segmentation

Abstract

How to learn long-range dependencies from 3D point clouds is a challenging problem in 3D point cloud analysis. Addressing this problem, we propose a global attention network for point cloud semantic segmentation, named as GA-Net, consisting of a point-independent global attention module and a point-dependent global attention module for obtaining contextual information of 3D point clouds in this paper. The point-independent global attention module simply shares a global attention map for all 3D points. In the point-dependent global attention module, for each point, a novel random cross attention block using only two randomly sampled subsets is exploited to learn the contextual information of all the points. Additionally, we design a novel point-adaptive aggregation block to replace linear skip connection for aggregating more discriminate features. Extensive experimental results on three 3D public datasets demonstrate that our method outperforms state-of-the-art methods in most cases.

References in corpus (1)

• GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond

Cited by in corpus (4)

• Beyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classification
• 3DMASC: Accessible, explainable 3D point clouds classification. Application to Bi-spectral Topo-bathymetric lidar data
• A Representation Separation Perspective to Correspondences-free Unsupervised 3D Point Cloud Registration
• Searching Dense Point Correspondences via Permutation Matrix Learning