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Xiu et al., 2018 - Google Patents

3D semantic segmentation for high-resolution aerial survey derived point clouds using deep learning

Xiu et al., 2018

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Document ID
15796318588049635865
Author
Xiu H
Vinayaraj P
Kim K
Nakamura R
Yan W
Publication year
Publication venue
Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

External Links

Snippet

Three-dimensional (3D) Semantic segmentation of aerial derived point cloud aims at assigning each point to a semantic class such as building, tree, road, and so on. Accurate 3D-segmentation results can be used as an essential information for constructing 3D city …
Continue reading at www.researchgate.net (PDF) (other versions)

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    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
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    • G06F17/30781Information retrieval; Database structures therefor; File system structures therefor of video data
    • G06F17/30784Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre
    • G06F17/30799Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre using low-level visual features of the video content
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