Meshtrics: Objective Quality Assessment of Textured 3D Meshes for 3D Reconstruction

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dc.contributor.authorMadeira, Tiagoen_US
dc.contributor.authorOliveira, Miguelen_US
dc.contributor.authorDias, Pauloen_US
dc.contributor.editorCaputo, Arielen_US
dc.contributor.editorGarro, Valeriaen_US
dc.contributor.editorGiachetti, Andreaen_US
dc.contributor.editorCastellani, Umbertoen_US
dc.contributor.editorDulecha, Tinsae Gebrechristosen_US
dc.date.accessioned2024-11-11T12:48:45Z
dc.date.available2024-11-11T12:48:45Z
dc.date.issued2024
dc.description.abstractIn the context of 3D reconstruction, the pursuit of photorealistic models requires precise, objective quality evaluation methods. In this work, we investigate several potential objective metrics for the quality assessment of textured 3D meshes by evaluating their correlation with human perception of visual quality. We conduct experiments using a publicly available, subjectively-rated database of textured 3D meshes containing various types of geometry and texture distortions. Based on these experiments, we discuss the characteristics and limitations of the evaluated metrics. Notably, image-based metrics demonstrated the strongest correlation with subjective scores in most tested scenarios, suggesting that 2D image metrics are reliable predictors of 3D model visual quality. We then introduce a framework designed to facilitate the analysis of various characteristics of 3D models and their fidelity, with a particular focus on image-based metrics leveraging photographs of real-world environments as reference. Our toolkit streamlines the generation of renders and the application of quality metrics, enabling manual annotation in 2D and 3D spaces, while incorporating an automatic alignment refinement step for precise registration of reference photographs. We evaluate the proposed approach using a dataset generated through the 3D reconstruction of a complex indoor environment. Our experiments support the efficacy of the solution in benchmarking 3D reconstruction results, enabling timely informed adjustments to the reconstruction methodology. Source code is available at https://github.com/tiagomfmadeira/Meshtrics.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationSmart Tools and Applications in Graphics - Eurographics Italian Chapter Conference
dc.identifier.doi10.2312/stag.20241351
dc.identifier.isbn978-3-03868-265-3
dc.identifier.issn2617-4855
dc.identifier.pages3 pages
dc.identifier.urihttps://doi.org/10.2312/stag.20241351
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/stag20241351
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Appearance and texture representations; Perception; Mesh models
dc.subjectComputing methodologies → Appearance and texture representations
dc.subjectPerception
dc.subjectMesh models
dc.titleMeshtrics: Objective Quality Assessment of Textured 3D Meshes for 3D Reconstructionen_US
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Italian Chapter Conference 2024 - Smart Tools and Apps in Graphics