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• W3199986655 abstract "Geometric data acquired from real-world scenes, <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>e.g.</i> , 2D depth images, 3D point clouds, and 4D dynamic point clouds, have found a wide range of applications including immersive telepresence, autonomous driving, surveillance, <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>etc</i> . Due to irregular sampling patterns of most geometric data, traditional image/video processing methodologies are limited, while Graph Signal Processing (GSP)—a fast-developing field in the signal processing community—enables processing signals that reside on irregular domains and plays a critical role in numerous applications of geometric data from low-level processing to high-level analysis. To further advance the research in this field, we provide the first timely and comprehensive overview of GSP methodologies for geometric data in a unified manner by bridging the connections between geometric data and graphs, among the various geometric data modalities, and with spectral/nodal graph filtering techniques. We also discuss the recently developed Graph Neural Networks (GNNs) and interpret the operation of these networks from the perspective of GSP. We conclude with a brief discussion of open problems and challenges." @default.
• W3199986655 created "2021-09-27" @default.
• W3199986655 creator A5016854114 @default.
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• W3199986655 date "2022-01-01" @default.
• W3199986655 modified "2023-10-18" @default.
• W3199986655 title "Graph Signal Processing for Geometric Data and Beyond: Theory and Applications" @default.
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• W3199986655 doi "https://doi.org/10.1109/tmm.2021.3111440" @default.
• W3199986655 hasPublicationYear "2022" @default.

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