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Adaptive bridge model for compressed domain point cloud classification

dc.contributor.authorSeleem, Abdelrahman
dc.contributor.authorGuarda, André F. R.
dc.contributor.authorRodrigues, Nuno M. M.
dc.contributor.authorPereira, Fernando
dc.date.accessioned2025-01-08T15:01:54Z
dc.date.available2025-01-08T15:01:54Z
dc.date.issued2024-06-08
dc.date.updated2024-12-28T10:15:07Z
dc.description.abstractThe recent adoption of deep learning-based models for the processing and coding of multimedia signals has brought noticeable gains in performance, which have established deep learning-based solutions as the uncontested state-of-the-art both for computer vision tasks, targeting machine consumption, as well as, more recently, coding applications, targeting human visualization. Traditionally, applications requiring both coding and computer vision processing require frst decoding the bitstream and then applying the computer vision methods to the decompressed multimedia signals. However, the adoption of deep learning-based solutions enables the use of compressed domain computer vision processing, with gains in performance and computational complexity over the decompressed domain approach. For point clouds (PCs), these gains have been demonstrated in the single available compressed domain computer vision processing solution, named Compressed Domain PC Classifer, which processes JPEG Pleno PC coding (PCC) compressed streams using a PC classifer largely compatible with the state-of-the-art spatial domain PointGrid classifer. However, the available Compressed Domain PC Classifer presents strong limitations by imposing a single, specifc input size which is associated to specifc JPEG Pleno PCC confgurations; this limits the compression performance as these confgurations are not ideal for all PCs due to their diferent characteristics, notably density. To overcome these limitations, this paper proposes the frst Adaptive Compressed Domain PC Classifer solution which includes a novel adaptive bridge model that allows to process the JPEG Pleno PCC encoded bit streams using diferent coding confgurations, now maximizing the compression efciency. Experimental results show that the novel Adaptive Compressed Domain PC Classifer allows JPEG PCC to achieve better compression performance by not imposing a single, specifc coding confguration for all PCs, regardless of its diferent characteristics. Moreover, the added adaptability power can achieve slightly better PC classifcation performance than the previous Compressed Domain PC Classifer and largely better PC classifcation performance (and lower number of weights) than the PointGrid PC classifer working in the decompressed domain.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationSeleem, A., Guarda, A.F.R., Rodrigues, N.M.M. et al. Adaptive bridge model for compressed domain point cloud classification. J Image Video Proc. 2024, 13 (2024). https://doi.org/10.1186/s13640-024-00631-6pt_PT
dc.identifier.doihttps://doi.org/10.1186/s13640-024-00631-6pt_PT
dc.identifier.eissn1687-5281
dc.identifier.slugcv-prod-4248122
dc.identifier.urihttp://hdl.handle.net/10400.8/10358
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringerOpenpt_PT
dc.relationDeep learning-based Point Cloud Representation
dc.relation.publisherversionhttps://jivp-eurasipjournals.springeropen.com/articles/10.1186/s13640-024-00631-6#citeaspt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectPoint cloudpt_PT
dc.subjectClassifcationpt_PT
dc.subjectCodingpt_PT
dc.subjectCompressed domainpt_PT
dc.subjectDeep learningpt_PT
dc.titleAdaptive bridge model for compressed domain point cloud classificationpt_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.endPage27pt_PT
oaire.citation.issue13pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleEURASIP Journal on Image and Video Processingpt_PT
oaire.citation.volume2024pt_PT
oaire.fundingStream3599-PPCDT
person.familyNameM. M. Rodrigues
person.givenNameNuno
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.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationb4ebe652-7f0e-4e67-adb0-d5ea29fc9e69
relation.isAuthorOfPublication.latestForDiscoveryb4ebe652-7f0e-4e67-adb0-d5ea29fc9e69
relation.isProjectOfPublicationa018adbc-131c-448f-acdc-fad90c525470
relation.isProjectOfPublication.latestForDiscoverya018adbc-131c-448f-acdc-fad90c525470

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