Título :	Local texture and geometry descriptors for fast block-based motion estimation of dynamic voxelized point clouds
Autor :	Dorea, Camilo Chang Hung, Edson Mintsu Queiroz, Ricardo Lopes de
metadata.dc.contributor.affiliation:	Universidade de Brasília, Departamento de Ciência da Computação Universidade de Brasília, Departamento de Engenharia Elétrica Universidade de Brasília, Departamento de Ciência da Computação
Assunto::	Nuvem de pontos Imagem tridimensional Estimativa de movimento
Fecha de publicación :	26-ago-2019
Editorial :	IEEE
Citación :	DOREA, Camilo; HUNG, Edson M.; QUEIROZ, Ricardo L. de. Local Texture and Geometry Descriptors for Fast Block-Based Motion Estimation of Dynamic Voxelized Point Clouds. 2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, 2019, Taipei, Taiwan: IEEE, 2019. p. 3721-3725, DOI: 10.1109/ICIP.2019.8803690.
Abstract:	Motion estimation in dynamic point cloud analysis or compression is a computationally intensive procedure generally involving a large search space and often complex voxel matching functions. We present an extension and improvement on prior work to speed up block-based motion estimation between temporally adjacent point clouds. We introduce local, or block-based, texture descriptors as a complement to voxel geometry description. Descriptors are organized in an occupancy map which may be efficiently computed and stored. By consulting the map, a point cloud motion estimator may significantly reduce its search space while maintaining prediction distortion at similar quality levels. The proposed texture-based occupancy maps provide significant speedup, an average of 26.9% for the tested data set, with respect to prior work.
metadata.dc.description.unidade:	Faculdade de Tecnologia (FT) Departamento de Engenharia Elétrica (FT ENE) Instituto de Ciências Exatas (IE) Departamento de Ciência da Computação (IE CIC)
DOI:	10.1109/ICIP.2019.8803690
metadata.dc.relation.publisherversion:	https://ieeexplore.ieee.org/document/8803690
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