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• W3155390614 abstract "Learning intra-region contexts and inter-region relations are two effective strategies to strengthen feature representations for point cloud analysis. However, unifying the two strategies for point cloud representation is not fully emphasized in existing methods. To this end, we propose a novel framework named Point Relation-Aware Network (PRA-Net), which is composed of an Intra-region Structure Learning (ISL) module and an Inter-region Relation Learning (IRL) module. The ISL module can dynamically integrate the local structural information into the point features, while the IRL module captures inter-region relations adaptively and efficiently via a differentiable region partition scheme and a representative point-based strategy. Extensive experiments on several 3D benchmarks covering shape classification, keypoint estimation, and part segmentation have verified the effectiveness and the generalization ability of PRA-Net. Code will be available at <uri xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>https://github.com/XiwuChen/PRA-Net</uri> ." @default.
• W3155390614 created "2021-04-26" @default.
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• W3155390614 date "2021-01-01" @default.
• W3155390614 modified "2023-10-14" @default.
• W3155390614 title "PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysis" @default.
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• W3155390614 doi "https://doi.org/10.1109/tip.2021.3072214" @default.
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