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Structure-Aware Graph Convolution Network for Point Cloud Parsing
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DOI：10.1109/TMM.2022.3216951.png)
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摘要
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Point clouds are becoming a popular medium to describe 3D scenes, benefitting from their accuracy and completeness in expressing the spatial and geometrical information of objects. However, due to the disorder and uneven distribution nature, merely selecting neighbors for point clouds in Euclidean space is inefficient and position-ignoring. To fill this gap, we propose a structure-aware graph convolution network (SA-GCN), which consists of an adaptive dilated KNN module (ADKNN), a learnable graph filter (LGF), and a structure-aware feature transformation module (SFT). Specially, the ADKNN module can dynamically adjust the range of grouping neighbor points, while being universal to improve the performance of arbitrary KNN-based methods. Moreover, with the localized auxiliary information provided by LGF, our SFT module disentangles the spatial details as a sort of coding guidance for better deep feature representations. Extensive experimental results on point cloud classification and segmentation tasks demonstrate the superiority of our proposed network.
Keyword:
Point cloud compression
Convolution
Task analysis
Kernel
Three-dimensional displays
Feature extraction
Decoding
Point cloud parsing
adaptive neighbor selection
shape message passing
feature-wise transformation
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9.7
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4.6K
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