Publication
Deep Learning-based Point Cloud Geometry Coding with Resolution Scalability
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 F. R. | |
dc.contributor.author | Rodrigues, Nuno M. M. | |
dc.contributor.author | Pereira, Fernando | |
dc.date.accessioned | 2025-07-22T15:31:03Z | |
dc.date.available | 2025-07-22T15:31:03Z | |
dc.date.issued | 2020-09 | |
dc.description | EISBN - 978-1-7281-9320-5 | |
dc.description | Article number - 9287060; Conference date - 21 September 2020 - 24 September 2020; Conference code - 165866 | |
dc.description.abstract | Point clouds are a 3D visual representation format that has recently become fundamentally important for immersive and interactive multimedia applications. Considering the high number of points of practically relevant point clouds, and their increasing market demand, efficient point cloud coding has become a vital research topic. In addition, scalability is an important feature for point cloud coding, especially for real-time applications, where the fast and rate efficient access to a decoded point cloud is important; however, this issue is still rather unexplored in the literature. In this context, this paper proposes a novel deep learning-based point cloud geometry coding solution with resolution scalability via interlaced sub-sampling. As additional layers are decoded, the number of points in the reconstructed point cloud increases as well as the overall quality. Experimental results show that the proposed scalable point cloud geometry coding solution outperforms the recent MPEG Geometry-based Point Cloud Compression standard which is much less scalable. | 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 EU funds under the project UIDB/EEA/50008/2020. | |
dc.identifier.citation | A. F. R. Guarda, N. M. M. Rodrigues and F. Pereira, "Deep Learning-based Point Cloud Geometry Coding with Resolution Scalability," 2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP), Tampere, Finland, 2020, pp. 1-6, doi: https://doi.org/10.1109/MMSP48831.2020.9287060. | |
dc.identifier.doi | 10.1109/mmsp48831.2020.9287060 | |
dc.identifier.eissn | 2473-3628 | |
dc.identifier.isbn | 978-1-7281-9323-6 | |
dc.identifier.isbn | 978-1-7281-9320-5 | |
dc.identifier.issn | 2163-3517 | |
dc.identifier.uri | http://hdl.handle.net/10400.8/13753 | |
dc.language.iso | eng | |
dc.peerreviewed | yes | |
dc.publisher | IEEE Canada | |
dc.relation | Efficient lossy and lossless compression of point clouds | |
dc.relation.hasversion | https://ieeexplore.ieee.org/document/9287060 | |
dc.relation.ispartof | 2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP) | |
dc.rights.uri | N/A | |
dc.subject | point cloud coding | |
dc.subject | deep learning | |
dc.subject | scalable coding | |
dc.subject | resolution scalability | |
dc.subject | interlaced sampling | |
dc.title | Deep Learning-based Point Cloud Geometry Coding with Resolution Scalability | eng |
dc.type | conference paper | |
dspace.entity.type | Publication | |
oaire.awardTitle | Efficient lossy and lossless compression of point clouds | |
oaire.awardURI | http://hdl.handle.net/10400.8/12910 | |
oaire.citation.conferenceDate | 2020-09 | |
oaire.citation.conferencePlace | Tampere, Finland | |
oaire.citation.title | IEEE 22nd International Workshop on Multimedia Signal Processing, MMSP 2020 | |
oaire.fundingStream | OE | |
oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
person.familyName | M. M. Rodrigues | |
person.givenName | Nuno | |
person.identifier.orcid | 0000-0001-9536-1017 | |
person.identifier.scopus-author-id | 7006052345 | |
relation.isAuthorOfPublication | b4ebe652-7f0e-4e67-adb0-d5ea29fc9e69 | |
relation.isAuthorOfPublication.latestForDiscovery | b4ebe652-7f0e-4e67-adb0-d5ea29fc9e69 | |
relation.isProjectOfPublication | d619b8c6-7ef9-4635-98fd-a9a7be25e5f8 | |
relation.isProjectOfPublication.latestForDiscovery | d619b8c6-7ef9-4635-98fd-a9a7be25e5f8 |
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- Point clouds are a 3D visual representation format that has recently become fundamentally important for immersive and interactive multimedia applications. Considering the high number of points of practically relevant point clouds, and their increasing market demand, efficient point cloud coding has become a vital research topic. In addition, scalability is an important feature for point cloud coding, especially for real-time applications, where the fast and rate efficient access to a decoded point cloud is important; however, this issue is still rather unexplored in the literature. In this context, this paper proposes a novel deep learning-based point cloud geometry coding solution with resolution scalability via interlaced sub-sampling. As additional layers are decoded, the number of points in the reconstructed point cloud increases as well as the overall quality. Experimental results show that the proposed scalable point cloud geometry coding solution outperforms the recent MPEG Geometry-based Point Cloud Compression standard which is much less scalable.
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