Título:	Block-based motion estimation speedup for dynamic voxelized point clouds
Autor(es):	Dórea, Camilo Chang Queiroz, Ricardo Lopes de
Assunto:	Computação em nuvem Imagem tridimensional
Data de publicação:	Out-2018
Referência:	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.
Abstract:	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.
Unidade Acadêmica:	Instituto de Ciências Exatas (IE) Departamento de Ciência da Computação (IE CIC)
DOI:	https://dx.doi.org/10.1109/ICIP.2018.8451647
Aparece nas coleções:	Trabalhos apresentados em evento

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