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- W3213663882 abstract "This chapter begins with the definition of graphs and graph signals in point cloud applications. It introduces two families of methodologies for point cloud processing, namely, traditional graph signal processing (GSP) methods and geometric deep learning methods. Point clouds describe the geometry of objects or scenes with a set of irregularly sampled three-dimensional (3D) points. The chapter focuses on graph spectral methods for point cloud processing. The functionality of nodal-domain graph filtering is to fuse the feature at each 3D point with the feature from its neighboring 3D points in the graph vertex domain. The chapter also focuses on graph convolutional neural network methods for point clouds. It discusses the applications of graph spectral methods in low-level point cloud processing, including restoration, resampling, compression and so on. The chapter presents the benefits of GSP in high-level point cloud understanding from learning-based paradigms." @default.
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- W3213663882 date "2021-08-05" @default.
- W3213663882 modified "2023-09-23" @default.
- W3213663882 title "Graph Spectral Point Cloud Processing" @default.
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- W3213663882 doi "https://doi.org/10.1002/9781119850830.ch7" @default.
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