A machine learning framework for full-reference 3D shape quality assessment

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dc.contributor.MCBUauthorÇipiloğlu Yıldız, Zeynep
dc.contributor.authorÇipiloğlu Yıldız, Zeynep
dc.contributor.authorÖztireli, A. Cengiz
dc.contributor.authorCapin, Tolga
dc.contributor.departmentFakülteler > Mühendislik Ve Doğa Bilimleri Fakültesi > Bilgisayar Mühendisliği Bölümü
dc.date.accessioned2025-01-08T11:53:31Z
dc.date.available2025-01-08T11:53:31Z
dc.date.issued2018
dc.description.abstractTo decide whether the perceived quality of a mesh is influenced by a certain modification such as compressionors implification, a metric for estimating the visual quality of 3D meshes is required. Today, machine learning and deep learning techniques are getting increasingly popular since they present efficient solutions to many complex problems. However, these techniques are not much utilized in the field of 3D shape perception. We propose a novel machine learning-based approach for evaluating the visual quality of 3D static meshes. The novelty of our study lies in incorporating crowdsourcing in a machine learning framework for visual quality evaluation. We deliberate that this is an elegant way since modeling human visual system processes is a tedious task and requires tuning many parameters. We employ crowdsourcing methodology for collecting data of quality evaluations and metric learning for drawing the best parameters that well correlate with the human perception. Experimental validation of the proposed metric reveals a promising correlation between the metric output and human perception. Results of our crowdsourcing experiments are publicly available for the community.
dc.description.citationÇipiloğlu Yıldız, Z., Öztireli A. C. ve Capin, T. (2018), A machine learning framework for full-reference 3D shape quality assessment, Almanya: Springer.
dc.identifier.DOI-IDhttps://doi.org/10.1007/s00371-018-1592-9
dc.identifier.ORC-ID0000-0003-4129-591X
dc.identifier.categoryOfPublishedMaterialMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.identifier.e-issn01782789
dc.identifier.indicesScopus (DOI)
dc.identifier.nameOfPublishedMaterialSpringer
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/26730
dc.language.isoen
dc.publisherSpringer Berlin Heidelberg
dc.rightsaçık erişim (open access)
dc.subjectVisual quality assessment
dc.subjectMesh quality
dc.subjectPerceptual computer graphics
dc.subjectCrowdsourcing
dc.subjectMetric learning
dc.titleA machine learning framework for full-reference 3D shape quality assessment
dc.typeMakale
oaire.citation.endPage139
oaire.citation.issue1
oaire.citation.startPage127
oaire.citation.volume36

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