Deep Learning-Based Point Cloud Coding: A Behavior and Performance Study

M. M. Rodrigues, Nuno; Guarda, André; Pereira, Fernando

Publication

Deep Learning-Based Point Cloud Coding: A Behavior and Performance Study

2019-10Journal article

datacite.subject.fos	Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg	09:Indústria, Inovação e Infraestruturas
dc.contributor.author	M. M. Rodrigues, Nuno
dc.contributor.author	Guarda, André
dc.contributor.author	Pereira, Fernando
dc.date.accessioned	2025-06-16T11:27:03Z
dc.date.available	2025-06-16T11:27:03Z
dc.date.issued	2019-10
dc.description	Article number 89462118th European Workshop on Visual Information Processing, EUVIP 2019 Rome28 October 2019through 31 October 2019Code 156571
dc.description.abstract	Point clouds are an emerging 3D visual representation model for immersive and interactive multimedia applications, inparticular for virtual and augmented reality. The huge amount of data associated to point clouds critically asks for efficient point cloud coding technology. While there are already some point cloud coding paradigms in the literature, notably octree, patch and graph-based for geometry data, very recently deep learning emerged in this research domain, offering very promising performances for image coding. While deep learning-based methods often provide interesting results, the understanding of this type of coding solutions is essential to improve their design in order to be used effectively. In this context, this paper presents a study and analysis on the behavior and performance of a deep learning-based point cloud coding solution based on an autoencoder network using only convolutional layers. Beside a promising RD performance, other findings should allow making	por
dc.description.sponsorship	European Regional Development Fund
dc.identifier.citation	Guarda, A. F., Rodrigues, N. M., & Pereira, F. (2019, October). Deep learning-based point cloud coding: A behavior and performance study. In 2019 8th European Workshop on Visual Information Processing (EUVIP) (pp. 34-39). IEEE. doi: 10.1109/EUVIP47703.2019.8946211.
dc.identifier.doi	10.1109/euvip47703.2019.8946211
dc.identifier.isbn	978-1-7281-4496-2
dc.identifier.issn	2471-8963
dc.identifier.issn	2164-974X
dc.identifier.uri	http://hdl.handle.net/10400.8/13255
dc.language.iso	eng
dc.peerreviewed	n/a
dc.publisher	IEEE Canada
dc.relation	Efficient lossy and lossless compression of point clouds
dc.relation	Instituto de Telecomunicações
dc.relation.hasversion	https://ieeexplore.ieee.org/document/8946211
dc.relation.ispartof	2019 8th European Workshop on Visual Information Processing (EUVIP)
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/
dc.subject	Three-dimensional displays
dc.subject	Encoding
dc.subject	Geometry
dc.subject	Image coding
dc.subject	Point cloud coding
dc.subject	Convolutional neural network
dc.title	Deep Learning-Based Point Cloud Coding: A Behavior and Performance Study
dc.type	journal article
dspace.entity.type	Publication
oaire.awardTitle	Efficient lossy and lossless compression of point clouds
oaire.awardTitle	Instituto de Telecomunicações
oaire.awardURI	http://hdl.handle.net/10400.8/12910
oaire.awardURI	info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FEEA%2F50008%2F2019/PT
oaire.citation.title	European Workshop on Visual Information Processing (EUVIP)
oaire.fundingStream	OE
oaire.fundingStream	6817 - DCRRNI ID
oaire.version	http://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyName	M. M. Rodrigues
person.familyName	Guarda
person.givenName	Nuno
person.givenName	André
person.identifier.ciencia-id	F811-146F-4EE9
person.identifier.orcid	0000-0001-9536-1017
person.identifier.orcid	0000-0001-5996-1074
person.identifier.scopus-author-id	7006052345
project.funder.identifier	http://doi.org/10.13039/501100001871
project.funder.name	Fundação para a Ciência e a Tecnologia
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