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Inteligentní návrh interiérů - Kompatibilita stylu 3D modelů nábytku pomocí neuronových sítí
dc.contributor.advisorMirbauer, Martin
dc.creatorSakaguchi, Yuu
dc.date.accessioned2020-02-24T09:38:09Z
dc.date.available2020-02-24T09:38:09Z
dc.date.issued2020
dc.identifier.urihttp://hdl.handle.net/20.500.11956/116604
dc.description.abstractThesis title: Intelligent Interior Design - Style Compatibility of 3D Furniture Models using Neural Networks Author: Yuu Sakaguchi Abstract: Analysis of 3D shapes is a challenging task especially when it comes to measuring the styles. There are numerous possible real-world applications which benefit from machine understanding of 3D objects, so we explore analytical models to measure style-related features. 3D models can be represented in different formats such as polygon mesh, multi-view images, and point cloud, and each of them has different characteristics. In this work, we mainly focus on analyzing the ability of a point cloud to represent style information. In addition, we replicate an existing multi-view based method in order to fairly compare the performance of different representations. The goal of this thesis is to explore and evaluate point cloud based methods, and apply our method to develop applications which provides easy search in a furniture database based on style similarity. We trained and tested our model on two datasets which contain several different categories of 3D objects such as furniture in dining rooms, furniture in living rooms, buildings, and coffee sets. As the available datasets do not provide style class labels, we learn the embedding using triplet architecture and triplet...en_US
dc.languageEnglishcs_CZ
dc.language.isoen_US
dc.publisherUniverzita Karlova, Matematicko-fyzikální fakultacs_CZ
dc.subject3D grafikacs_CZ
dc.subjectneuronové sítěcs_CZ
dc.subject3D graphicsen_US
dc.subjectneural networksen_US
dc.subjectmetric learningen_US
dc.subjectstyle similarity;en_US
dc.titleIntelligent Interior Design - Style Compatibility of 3D Furniture Models using Neural Networksen_US
dc.typediplomová prácecs_CZ
dcterms.created2020
dcterms.dateAccepted2020-02-03
dc.description.departmentKatedra softwaru a výuky informatikycs_CZ
dc.description.departmentDepartment of Software and Computer Science Educationen_US
dc.description.facultyFaculty of Mathematics and Physicsen_US
dc.description.facultyMatematicko-fyzikální fakultacs_CZ
dc.identifier.repId215649
dc.title.translatedInteligentní návrh interiérů - Kompatibilita stylu 3D modelů nábytku pomocí neuronových sítícs_CZ
dc.contributor.refereeStřelský, Jakub
thesis.degree.nameMgr.
thesis.degree.levelnavazující magisterskécs_CZ
thesis.degree.disciplineArtificial Intelligenceen_US
thesis.degree.disciplineUmělá inteligencecs_CZ
thesis.degree.programComputer Scienceen_US
thesis.degree.programInformatikacs_CZ
uk.thesis.typediplomová prácecs_CZ
uk.taxonomy.organization-csMatematicko-fyzikální fakulta::Katedra softwaru a výuky informatikycs_CZ
uk.taxonomy.organization-enFaculty of Mathematics and Physics::Department of Software and Computer Science Educationen_US
uk.faculty-name.csMatematicko-fyzikální fakultacs_CZ
uk.faculty-name.enFaculty of Mathematics and Physicsen_US
uk.faculty-abbr.csMFFcs_CZ
uk.degree-discipline.csUmělá inteligencecs_CZ
uk.degree-discipline.enArtificial Intelligenceen_US
uk.degree-program.csInformatikacs_CZ
uk.degree-program.enComputer Scienceen_US
thesis.grade.csVýborněcs_CZ
thesis.grade.enExcellenten_US
uk.abstract.enThesis title: Intelligent Interior Design - Style Compatibility of 3D Furniture Models using Neural Networks Author: Yuu Sakaguchi Abstract: Analysis of 3D shapes is a challenging task especially when it comes to measuring the styles. There are numerous possible real-world applications which benefit from machine understanding of 3D objects, so we explore analytical models to measure style-related features. 3D models can be represented in different formats such as polygon mesh, multi-view images, and point cloud, and each of them has different characteristics. In this work, we mainly focus on analyzing the ability of a point cloud to represent style information. In addition, we replicate an existing multi-view based method in order to fairly compare the performance of different representations. The goal of this thesis is to explore and evaluate point cloud based methods, and apply our method to develop applications which provides easy search in a furniture database based on style similarity. We trained and tested our model on two datasets which contain several different categories of 3D objects such as furniture in dining rooms, furniture in living rooms, buildings, and coffee sets. As the available datasets do not provide style class labels, we learn the embedding using triplet architecture and triplet...en_US
uk.file-availabilityV
uk.grantorUniverzita Karlova, Matematicko-fyzikální fakulta, Katedra softwaru a výuky informatikycs_CZ
thesis.grade.code1
uk.publication-placePrahacs_CZ


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