Publication
Point Cloud Coding: Adopting a Deep Learningbased Approach
| datacite.subject.fos | Ciências Médicas::Biotecnologia Médica | |
| datacite.subject.fos | Engenharia e Tecnologia::Engenharia Médica | |
| datacite.subject.sdg | 03:Saúde de Qualidade | |
| datacite.subject.sdg | 10:Reduzir as Desigualdades | |
| datacite.subject.sdg | 11:Cidades e Comunidades Sustentáveis | |
| dc.contributor.author | Guarda, André | |
| dc.contributor.author | M. M. Rodrigues, Nuno | |
| dc.contributor.author | Pereira, Fernando | |
| dc.date.accessioned | 2025-05-20T14:35:08Z | |
| dc.date.available | 2025-05-20T14:35:08Z | |
| dc.date.issued | 2019-11 | |
| dc.description | Conference: Picture Coding Symposium (PCS), Nov. 12-15, 2019, Ningbo, China, 2019 | |
| dc.description.abstract | Point clouds have recently become an important visual representation format, especially for virtual and augmented reality applications, thus making point cloud coding a very hot research topic. Deep learning-based coding methods have recently emerged in the field of image coding with increasing success. These coding solutions take advantage of the ability of convolutional neural networks to extract adaptive features from the images to create a latent representation that can be efficiently coded. In this context, this paper extends the deep-learning coding approach to point cloud coding using an autoencoder network design. Performance results are very promising, showing improvements over the Point Cloud Library codec often taken as benchmark, thus suggesting a significant margin of evolution for this new point cloud coding paradigm. | eng |
| dc.description.sponsorship | This work was funded by Fundação para a Ciência e Tecnologia (FCT), Portugal, Ph.D. Grant SFRH/BD/118218/2016, by FCT/MEC through national funds and when applicable co-funded by FEDER – PT2020 partnership agreement under the project UID/EEA/50008/2019. | |
| dc.identifier.citation | André, G., M., R. N. M., & Fernando, P. (2019). Point Cloud Coding: Adopting a Deep Learning-based Approach. Picture Coding Symposium (PCS). CN. https://doi.org/10.1109/PCS48520.2019.8954537 | |
| dc.identifier.doi | 10.1109/pcs48520.2019.8954537 | |
| dc.identifier.isbn | 978-1-7281-4705-5 | |
| dc.identifier.issn | 2330-7935 | |
| dc.identifier.uri | http://hdl.handle.net/10400.8/12943 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| 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/8954537 | |
| dc.relation.ispartof | 2019 Picture Coding Symposium (PCS) | |
| dc.rights.uri | N/A | |
| dc.subject | point cloud coding | |
| dc.subject | deep learning | |
| dc.subject | convolutional neural network | |
| dc.title | Point Cloud Coding: Adopting a Deep Learningbased Approach | eng |
| 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 | Picture Coding Symposium (PCS) | |
| oaire.fundingStream | OE | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Guarda | |
| person.familyName | M. M. Rodrigues | |
| person.givenName | André | |
| person.givenName | Nuno | |
| person.identifier.ciencia-id | F811-146F-4EE9 | |
| person.identifier.orcid | 0000-0001-5996-1074 | |
| person.identifier.orcid | 0000-0001-9536-1017 | |
| 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 | |
| relation.isAuthorOfPublication | ab4d7e6e-b391-49ba-a618-a52fc62c8837 | |
| relation.isAuthorOfPublication | b4ebe652-7f0e-4e67-adb0-d5ea29fc9e69 | |
| relation.isAuthorOfPublication.latestForDiscovery | b4ebe652-7f0e-4e67-adb0-d5ea29fc9e69 | |
| relation.isProjectOfPublication | d619b8c6-7ef9-4635-98fd-a9a7be25e5f8 | |
| relation.isProjectOfPublication | 8dd76ecc-a109-45e6-9fda-f72c712a17b9 | |
| relation.isProjectOfPublication.latestForDiscovery | d619b8c6-7ef9-4635-98fd-a9a7be25e5f8 |