Titre:	Fractional super-resolution of voxelized point clouds
Auteur(s):	Borges, Tomás Malheiros Garcia, Diogo Caetano Queiroz, Ricardo Lopes de
metadata.dc.identifier.orcid:	https://orcid.org/0000-0003-0182-1866 https://orcid.org/0000-0002-3816-0873 https://orcid.org/0000-0002-3911-1838
Assunto::	Nuvens de pontos Super-resolução
Date de publication:	2022
Editeur:	IEEE
Référence bibliographique:	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.
Abstract:	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.
Licença::	© Copyright 2022 IEEE - All rights reserved.
DOI:	https://doi.org/10.1109/TIP.2022.3141611
metadata.dc.relation.publisherversion:	https://ieeexplore.ieee.org/document/9682535
Collection(s) :	Artigos publicados em periódicos e afins

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