DeepTex: Deep Learning-Based Texturing of Image-Based 3D Reconstructions

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dc.contributor.authorNeumann, Kai Alexanderen_US
dc.contributor.authorSantos, Pedroen_US
dc.contributor.authorFellner, Dieter W.en_US
dc.contributor.editorCorsini, Massimilianoen_US
dc.contributor.editorFerdani, Danieleen_US
dc.contributor.editorKuijper, Arjanen_US
dc.contributor.editorKutlu, Hasanen_US
dc.date.accessioned2024-09-15T09:57:14Z
dc.date.available2024-09-15T09:57:14Z
dc.date.issued2024
dc.description.abstractImage-based 3D reconstruction is a commonly used technique for measuring the geometry and color of objects or scenes based on images. While the geometry reconstruction of state-of-the-art approaches is mostly robust against varying lighting conditions and outliers, these pose a significant challenge for calculating an accurate texture map. This work proposes a deep-learning based texturing approach called ''DeepTex'' that uses a custom learned blending method on top of a traditional mosaic-based texturing approach. The model was trained using a custom synthetic data generation workflow and showed a significantly increased accuracy when generating textures in the presence of outliers and non-uniform lighting.en_US
dc.description.sectionheadersColour, AI-based Styling and Texturing
dc.description.seriesinformationEurographics Workshop on Graphics and Cultural Heritage
dc.identifier.doi10.2312/gch.20241257
dc.identifier.isbn978-3-03868-248-6
dc.identifier.issn2312-6124
dc.identifier.pages4 pages
dc.identifier.urihttps://doi.org/10.2312/gch.20241257
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/gch20241257
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 → Reconstruction; Computer vision; Artificial intelligence
dc.subjectComputing methodologies → Reconstruction
dc.subjectComputer vision
dc.subjectArtificial intelligence
dc.titleDeepTex: Deep Learning-Based Texturing of Image-Based 3D Reconstructionsen_US
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GCH 2024 - Eurographics Workshop on Graphics and Cultural Heritage