Fractional super-resolution of voxelized point clouds

Borges, Tomás Malheiros; Garcia, Diogo Caetano; Queiroz, Ricardo Lopes de

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Campo DC	Valor	Idioma
dc.contributor.author	Borges, Tomás Malheiros	-
dc.contributor.author	Garcia, Diogo Caetano	-
dc.contributor.author	Queiroz, Ricardo Lopes de	-
dc.date.accessioned	2022-05-09T11:20:41Z	-
dc.date.available	2022-05-09T11:20:41Z	-
dc.date.issued	2022	-
dc.identifier.citation	BORGES, Tomás M; GARCIA, Diogo Caetano; QUEIROZ, Ricardo Lopes de. Fractional Super-Resolution of Voxelized Point Clouds. In: IEEE Transactions on Image Processing, v. 31, p. 1380-1390, 2022. DOI: https://doi.org/10.1109/TIP.2022.3141611. Disponível: https://ieeexplore.ieee.org/document/9682535. Acesso em: 9 maio 2022.	pt_BR
dc.identifier.uri	https://repositorio.unb.br/handle/10482/43670	-
dc.language.iso	Inglês	pt_BR
dc.publisher	IEEE	pt_BR
dc.rights	Acesso Restrito	pt_BR
dc.title	Fractional super-resolution of voxelized point clouds	pt_BR
dc.type	Artigo	pt_BR
dc.subject.keyword	Nuvens de pontos	pt_BR
dc.subject.keyword	Super-resolução	pt_BR
dc.rights.license	© Copyright 2022 IEEE - All rights reserved.	pt_BR
dc.identifier.doi	https://doi.org/10.1109/TIP.2022.3141611	pt_BR
dc.relation.publisherversion	https://ieeexplore.ieee.org/document/9682535	pt_BR
dc.description.abstract1	We present a method to super-resolve voxelized point clouds downsampled by a fractional factor, using lookup-tables (LUT) constructed from self-similarities from their own downsampled neighborhoods. The proposed method was developed to densify and to increase the precision of voxelized point clouds, and can be used, for example, as improve compression and rendering. We super-resolve the geometry, but we also interpolate texture by averaging colors from adjacent neighbors, for completeness. Our technique, as we understand, is the first specifically developed for intra-frame super-resolution of voxelized point clouds, for arbitrary resampling scale factors. We present extensive test results over different point clouds, showing the effectiveness of the proposed approach against baseline methods.	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0003-0182-1866	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0002-3816-0873	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0002-3911-1838	pt_BR
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