| Campo DC | Valor | Idioma |
|---|
| dc.contributor.author | Dórea, Camilo Chang | - |
| dc.contributor.author | Queiroz, Ricardo Lopes de | - |
| dc.date.accessioned | 2018-12-04T12:31:35Z | - |
| dc.date.available | 2018-12-04T12:31:35Z | - |
| dc.date.issued | 2018-10 | - |
| dc.identifier.citation | DOREA, Camilo; QUEIROZ, Ricardo L. de. Block-based motion estimation speedup for dynamic voxelized point clouds. In: IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 25., 2018, Atenas. Papers [...]. Atenas: IEEE, 2018. DOI: 10.1109/ICIP.2018.8451647. Disponível em: https://ieeexplore.ieee.org/document/8451647. Acesso em: 04 dez. 2018. | pt_BR |
| dc.identifier.uri | http://repositorio.unb.br/handle/10482/33120 | - |
| dc.language.iso | Inglês | pt_BR |
| dc.rights | Acesso Aberto | pt_BR |
| dc.title | Block-based motion estimation speedup for dynamic voxelized point clouds | pt_BR |
| dc.type | Trabalho | pt_BR |
| dc.subject.keyword | Computação em nuvem | pt_BR |
| dc.subject.keyword | Imagem tridimensional | pt_BR |
| dc.identifier.doi | https://dx.doi.org/10.1109/ICIP.2018.8451647 | pt_BR |
| dc.description.abstract1 | Motion 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. | pt_BR |
| dc.description.unidade | Instituto de Ciências Exatas (IE) | pt_BR |
| dc.description.unidade | Departamento de Ciência da Computação (IE CIC) | pt_BR |
| Aparece nas coleções: | Trabalhos apresentados em evento
|
