Attention-based relation and context modeling for point cloud semantic segmentation. (August 2020)

• Record Type: Journal Article
• Authors: Hu, Zhiyu; Zhang, Dongbo; Li, Shuai; Qin, Hong
• Is Part Of: Computers & graphics. Volume 90(2020)
• Page Start: 126
• Page End: 134
• Publication Date: 2020-08
• Subjects: Point cloud -- Deep learning -- Semantic segmentation -- Contextual fusion; Computer graphics -- Periodicals; 006.6
• DOI: 10.1016/j.cag.2020.06.001
• Languages: English
• ISSNs: 0097-8493

Abstract

Highlights: Attention-based local relation learning module can dynamically explore semantic relation in an anisotropic way. Context-guided aggregation module further enhances the distinguishing ability of points in feature space. Gated propagation strategy flexibly filters out irrelevant and redundant information between encoder and decoder. Multi-scale supervision boosts the training process. Graphical abstract: Abstract: Semantic segmentation of point cloud is a fundamental problem in scene-level understanding. Despite advancement in recent years by leveraging capabilities of Neural Networks and massive labeling datasets available, providing fine-grained semantic segmentation for point cloud is still challenging, given the fact that point cloud is usually unstructured, unordered and sparse. In this paper, we achieve semantic point cloud labeling by adaptively exploring semantic relation and aggregating contextual information between points. Specifically, we first introduce an attention-based local relation learning module for collecting local features, which can capture semantic relation in a manner of anisotropy. And we then design a novel context aggregation module guided by multi-scale supervision to obtain long-range dependencies between semantically-correlated points and enhance the distinctive ability of points in feature space. In addition, a gated propagation strategy is adopted instead of skip links to conditionally concatenate local point features in different layers. We empirically evaluate our method on public benchmarks (S3DIS and ShapeNetPart), and demonstrate our performance is on par or better than state-of-the-art methods.