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Deep Learning-Based Point Cloud Coding and Super-Resolution: a Joint Geometry and Color Approach

2023-11Journal article Restricted access
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dc.contributor.authorGuarda, André F. R.
dc.contributor.authorRuivo, Manuel
dc.contributor.authorCoelho, Luís
dc.contributor.authorSeleem, Abdelrahman
dc.contributor.authorM. M. Rodrigues, Nuno
dc.contributor.authorPereira, Fernando
dc.date.accessioned2025-01-08T14:36:11Z
dc.date.available2025-01-08T14:36:11Z
dc.date.issued2023-11
dc.date.updated2024-12-28T10:13:13Z
dc.description.abstractIn this golden age of multimedia, realistic content is in high demand with users seeking more immersive and interactive experiences. As a result, new image modalities for 3D representations have emerged in recent years, among which point clouds have deserved especial attention. Naturally, with this increase in demand, efficient storage and transmission became a must, with standardization groups such as MPEG and JPEG entering the scene, as it happened before with other types of visual media. In a surprising development, JPEG issued a Call for Proposals on point cloud coding targeting exclusively learningbased solutions, in parallel to a similar call for image coding. This is a natural consequence of the growing popularity of deep learning, which due to its excellent performances is currently dominant in the multimedia processing field, including coding. This paper presents the coding solution selected by JPEG as the best-performing response to the Call for Proposals and adopted as the first version of the JPEG Pleno Point Cloud Coding Verification Model, in practice the first step for developing a standard. The proposed solution offers a novel joint geometry and color approach for point cloud coding, in which a single deep learning model processes both geometry and color simultaneously. To maximize the RD performance for a large range of point clouds, the proposed solution uses down-sampling and learningbased super-resolution as pre- and post-processing steps. Compared to the MPEG point cloud coding standards, the proposed coding solution comfortably outperforms G-PCC, for both geometry, color, and joint quality metrics.pt_PT
dc.description.abstractIndex Terms—Deep learning, geometry and color, JPEG Pleno standard, point cloud coding, point cloud super-resolutionpt_PT
dc.description.versionN/Apt_PT
dc.identifier.citationA. F. R. Guarda, M. Ruivo, L. Coelho, A. Seleem, N. M. M. Rodrigues and F. Pereira, "Deep Learning-Based Point Cloud Coding and Super-Resolution: a Joint Geometry and Color Approach," in IEEE Transactions on Multimedia, doi: 10.1109/TMM.2023.3338081.pt_PT
dc.identifier.doi10.1109/tmm.2023.3338081pt_PT
dc.identifier.eissn1941-0077
dc.identifier.issn1520-9210
dc.identifier.slugcv-prod-3973864
dc.identifier.urihttp://hdl.handle.net/10400.8/10355
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEEpt_PT
dc.relationDeep learning-based Point Cloud Representation
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/10336534/keywords#keywordspt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectDeep learningpt_PT
dc.subjectGeometry and colorpt_PT
dc.subjectJPEG Pleno standardpt_PT
dc.subjectPoint cloud codingpt_PT
dc.subjectPoint cloud super-resolutionpt_PT
dc.titleDeep Learning-Based Point Cloud Coding and Super-Resolution: a Joint Geometry and Color Approachpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleDeep learning-based Point Cloud Representation
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FEEI-COM%2F1125%2F2021/PT
oaire.citation.endPage13pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleIEEE Transactions on Multimediapt_PT
oaire.fundingStream3599-PPCDT
person.familyNameGuarda
person.familyNameM. M. Rodrigues
person.givenNameAndré
person.givenNameNuno
person.identifier.ciencia-idF811-146F-4EE9
person.identifier.orcid0000-0001-5996-1074
person.identifier.orcid0000-0001-9536-1017
person.identifier.scopus-author-id7006052345
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.cv.cienciaid6917-B121-4E34 | NUNO MIGUEL MORAIS RODRIGUES
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationab4d7e6e-b391-49ba-a618-a52fc62c8837
relation.isAuthorOfPublicationb4ebe652-7f0e-4e67-adb0-d5ea29fc9e69
relation.isAuthorOfPublication.latestForDiscoveryab4d7e6e-b391-49ba-a618-a52fc62c8837
relation.isProjectOfPublicationa018adbc-131c-448f-acdc-fad90c525470
relation.isProjectOfPublication.latestForDiscoverya018adbc-131c-448f-acdc-fad90c525470

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