Publication
Adaptive Deep Learning-Based Point Cloud Geometry Coding
| dc.contributor.author | Andre F. R. Guarda | |
| dc.contributor.author | Rodrigues, Nuno M. M. | |
| dc.contributor.author | Fernando Pereira | |
| dc.date.accessioned | 2022-11-10T15:07:14Z | |
| dc.date.available | 2022-11-10T15:07:14Z | |
| dc.date.issued | 2021-02 | |
| dc.date.updated | 2022-11-08T17:27:37Z | |
| dc.description.abstract | Point clouds are a very rich 3D visual representation model, which has become increasingly appealing for multimedia applications with immersion, interaction and realism requirements. Due to different acquisition and creation conditions as well as target applications, point clouds’ characteristics may be very diverse, notably on their density. While geographical information systems or autonomous driving applications may use rather sparse point clouds, cultural heritage or virtual reality applications typically use denser point clouds to more accurately represent objects and people. Naturally, to offer immersion and realism, point clouds need a rather large number of points, thus asking for the development of efficient coding solutions. The use of deep learning models for coding purposes has recently gained relevance, with latest developments in image coding achieving state-of-the-art performance, thus making natural the adoption of this technology also for point cloud coding. This paper presents a novel deep learning-based solution for point cloud geometry coding which is able to efficiently adapt to the content’s characteristics. The proposed coding solution divides the point cloud into 3D blocks and selects the most suitable available deep learning coding model to code each block, thus maximizing the compression performance. In comparison to the state-of-the-art MPEG G-PCC Trisoup standard, the proposed coding solution offers average quality gains up to 4.9dB and 5.7dB for PSNR D1 and PSNR D2, respectively. | pt_PT |
| dc.description.version | N/A | pt_PT |
| dc.identifier.doi | 10.1109/JSTSP.2020.3047520 | pt_PT |
| dc.identifier.issn | 1941-0484 | |
| dc.identifier.issn | 1932-4553 | |
| dc.identifier.slug | cv-prod-2569819 | |
| dc.identifier.uri | http://hdl.handle.net/10400.8/7845 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.relation.publisherversion | https://ieeexplore.ieee.org/document/9309023 | pt_PT |
| dc.subject | Point cloud coding | pt_PT |
| dc.subject | Deep learning | pt_PT |
| dc.subject | Adaptive models | pt_PT |
| dc.title | Adaptive Deep Learning-Based Point Cloud Geometry Coding | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 430 | pt_PT |
| oaire.citation.issue | 2 | pt_PT |
| oaire.citation.startPage | 415 | pt_PT |
| oaire.citation.title | IEEE Journal of Selected Topics in Signal Processing | pt_PT |
| oaire.citation.volume | 15 | pt_PT |
| person.familyName | M. M. Rodrigues | |
| person.givenName | Nuno | |
| person.identifier.orcid | 0000-0001-9536-1017 | |
| person.identifier.scopus-author-id | 7006052345 | |
| rcaap.cv.cienciaid | 6917-B121-4E34 | NUNO MIGUEL MORAIS RODRIGUES | |
| rcaap.rights | restrictedAccess | pt_PT |
| rcaap.type | article | pt_PT |
| relation.isAuthorOfPublication | b4ebe652-7f0e-4e67-adb0-d5ea29fc9e69 | |
| relation.isAuthorOfPublication.latestForDiscovery | b4ebe652-7f0e-4e67-adb0-d5ea29fc9e69 |
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