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The JPEG Pleno Learning-Based Point Cloud Coding Standard: Serving Man and Machine

2025-03Journal article Open access
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datacite.subject.fosEngenharia e Tecnologia
datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
dc.contributor.authorGuarda, André
dc.contributor.authorM. M. Rodrigues, Nuno
dc.contributor.authorPereira, Fernando
dc.date.accessioned2025-07-15T16:49:30Z
dc.date.available2025-07-15T16:49:30Z
dc.date.issued2025-03
dc.date.updated2025-07-14T16:50:31Z
dc.descriptionInformation and data sciences: https://www.it.pt/Publications/PaperJournal/34608
dc.description.abstractEfficient point cloud coding has become increasingly critical for multiple applications such as virtual reality, autonomous driving, and digital twin systems, where rich and interactive 3D data representations may functionally make the difference. Deep learning has emerged as a powerful tool in this domain, offering advanced techniques for compressing point clouds more efficiently than conventional coding methods while also allowing effective computer vision tasks performed in the compressed domain thus, for the first time, making available a common compressed visual representation effective for both man and machine. Taking advantage of this potential, JPEG has recently finalized the JPEG Pleno Learning-based Point Cloud Coding (PCC) standard offering efficient lossy coding of static point clouds, targeting both human visualization and machine processing by leveraging deep learning models for geometry and color coding. The geometry is processed directly in its original 3D form using sparse convolutional neural networks, while the color data is projected onto 2D images and encoded using the also learning-based JPEG AI standard. The goal of this paper is to provide a complete technical description of the JPEG PCC standard, along with a thorough benchmarking of its performance against the state-of-the-art, while highlighting its main strengths and weaknesses. In terms of compression performance, JPEG PCC outperforms the conventional MPEG PCC standards, especially in geometry coding, achieving significant rate reductions. Color compression performance is less competitive but this is overcome by the power of a full learning-based coding framework for both geometry and color and the associated effective compressed domain processing.eng
dc.description.sponsorshipThis work was supported by Fundação para a Ciência e Tecnologia, I.P. (FCT, Funder ID = 50110000187), under the project with reference UIDB/50008/2020 [Digital Object Identifier (DOI): 10.54499/UIDB/50008/2020] and the project with reference PTDC/EEI-COM/1125/2021 entitled ‘‘Deep Learning-based Point Cloud Representation.’
dc.description.versionN/A
dc.identifier.citationA. F. R. Guarda, N. M. M. Rodrigues and F. Pereira, "The JPEG Pleno Learning-Based Point Cloud Coding Standard: Serving Man and Machine," in IEEE Access, vol. 13, pp. 43289-43315, 2025, doi: 10.1109/ACCESS.2025.3549316.
dc.identifier.doi10.1109/access.2025.3549316en_US
dc.identifier.issn2169-3536en_US
dc.identifier.slugcv-prod-4528488
dc.identifier.urihttp://hdl.handle.net/10400.8/13661
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE
dc.relationInstituto de Telecomunicações
dc.relationDeep learning-based Point Cloud Representation
dc.relation.hasversionhttps://ieeexplore.ieee.org/document/10916627
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectJPEGPleno standard
dc.subjectlearning-based coding
dc.subjectman and machine
dc.subjectpoint cloud coding
dc.titleThe JPEG Pleno Learning-Based Point Cloud Coding Standard: Serving Man and Machineeng
dc.typejournal articleen_US
dspace.entity.typePublication
oaire.awardTitleInstituto de Telecomunicações
oaire.awardTitleDeep learning-based Point Cloud Representation
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50008%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FEEI-COM%2F1125%2F2021/PT
oaire.citation.endPage43315
oaire.citation.startPage43289
oaire.citation.titleIEEE Accessen_US
oaire.citation.volume13en_US
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream3599-PPCDT
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
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.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.cv.cienciaid6917-B121-4E34 | NUNO MIGUEL MORAIS RODRIGUES
rcaap.rightsopenAccessen_US
relation.isAuthorOfPublicationab4d7e6e-b391-49ba-a618-a52fc62c8837
relation.isAuthorOfPublicationb4ebe652-7f0e-4e67-adb0-d5ea29fc9e69
relation.isAuthorOfPublication.latestForDiscoveryab4d7e6e-b391-49ba-a618-a52fc62c8837
relation.isProjectOfPublication0836c6a6-afd0-499e-8a16-612dd27ec1dc
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