Block-based motion estimation speedup for dynamic voxelized point clouds

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorDórea, Camilo Chang-
Autor(es): dc.creatorQueiroz, Ricardo Lopes de-
Data de aceite: dc.date.accessioned2021-10-14T18:17:04Z-
Data de disponibilização: dc.date.available2021-10-14T18:17:04Z-
Data de envio: dc.date.issued2018-12-04-
Data de envio: dc.date.issued2018-12-04-
Data de envio: dc.date.issued2018-10-
Fonte completa do material: dc.identifierhttp://repositorio.unb.br/handle/10482/33120-
Fonte completa do material: dc.identifierhttps://dx.doi.org/10.1109/ICIP.2018.8451647-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/627892-
Descrição: dc.descriptionMotion estimation is a key component in dynamic point cloud analysis and compression. We present a method for reducing motion estimation computation when processing block-based partitions of temporally adjacent point clouds. We propose the use of an occupancy map containing information regarding size or other higher-order local statistics of the partitions. By consulting the map, the estimator may significantly reduce its search space, avoiding expensive block-matching evaluations. To form the maps we use 3D moment descriptors efficiently computed with one-pass update formulas and stored as scalar-values for multiple, subsequent references. Results show that a speedup of 2 produces a maximum distortion dropoff of less than 2% for the adopted PSNR-based metrics, relative to distortion of predictions attained from full search. Speedups of 5 and 10 are achievable with small average distortion dropoffs, less than 3% and 5%, respectively, for the tested data set.-
Formato: dc.formatapplication/pdf-
Direitos: dc.rightsAcesso Aberto-
Palavras-chave: dc.subjectComputação em nuvem-
Palavras-chave: dc.subjectImagem tridimensional-
Título: dc.titleBlock-based motion estimation speedup for dynamic voxelized point clouds-
Aparece nas coleções:Repositório Institucional – UNB

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