2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper ID3D-1.4
Paper Title ATTENTION-BASED LOCAL REGION AGGREGATION NETWORK FOR HIERARCHICAL POINT CLOUD LEARNING
Authors Gaojie Chen, Ran Sun, Jie Ma, Bingli Wu, Huazhong University of Science and Technology, China
Session3D-1: Point Cloud Processing 1
LocationArea J
Session Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Poster
Topic Three-Dimensional Image and Video Processing: Point cloud processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract In point cloud processing, efficient feature aggregation of local regions is essential for hierarchical representations of 3D shapes. However, most prior works directly aggregate local features by symmetric functions, which will result in the desertion of vital shape information. In this paper, we propose a novel aggregation module based on 3D attention mechanism, named Local Region Attention Aggregation, which can capture the local shape implied in irregular points by emphasizing informative features and suppressing unnecessary ones. Specifically, for each local region, our module first encodes point relations, and then sequentially infers two attention maps generated by 3D geometric relations and fusion of multi-scale features respectively. After that, we multiply encoded point features with the attention maps to refine features adaptively. Consequently, by aggregating the higher-quality local features, shape awareness can be enhanced. Extensive experiments on various challenging benchmarks verify our method achieves state-of-the-art performance.