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- W4283257200 abstract "3D point cloud processing is challenging, as the points in the point cloud are disordered and irregularly distributed. Graph-based networks leverage the underlying topological relationship between points and achieve satisfactory performance in point cloud classification task. However, we observe that traditional graph construction and aggregation methods limit their efficiency. To address this problem, we propose a Sparse Graph Convolution Neural Network (SGCNN). Specifically, we apply a Sparse Graph Convolution (SGC) module to reduce the computation complexity of graph convolution and a Sparse Feature Encoding (SFE) module to enrich the representation of the point cloud in terms of sparse neighbor. The classification performances on synthetic and real-world benchmarks demonstrate the superiority and effectiveness of the proposed method. Compared with state-of-the-art methods, our approach balances accuracy and efficiency." @default.
- W4283257200 created "2022-06-23" @default.
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- W4283257200 date "2022-02-18" @default.
- W4283257200 modified "2023-10-14" @default.
- W4283257200 title "SGCNN for 3D Point Cloud Classification" @default.
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- W4283257200 doi "https://doi.org/10.1145/3529836.3529847" @default.
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