Deep SVBRDF Acquisition and Modelling: A Survey

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dc.contributor.authorKavoosighafi, Behnazen_US
dc.contributor.authorHajisharif, Saghien_US
dc.contributor.authorMiandji, Ehsanen_US
dc.contributor.authorBaravdish, Gabrielen_US
dc.contributor.authorCao, Wenen_US
dc.contributor.authorUnger, Jonasen_US
dc.contributor.editorAlliez, Pierreen_US
dc.contributor.editorWimmer, Michaelen_US
dc.date.accessioned2024-12-19T11:15:52Z
dc.date.available2024-12-19T11:15:52Z
dc.date.issued2024
dc.description.abstractHand in hand with the rapid development of machine learning, deep learning and generative AI algorithms and architectures, the graphics community has seen a remarkable evolution of novel techniques for material and appearance capture. Typically, these machine‐learning‐driven methods and technologies, in contrast to traditional techniques, rely on only a single or very few input images, while enabling the recovery of detailed, high‐quality measurements of bi‐directional reflectance distribution functions, as well as the corresponding spatially varying material properties, also known as Spatially Varying Bi‐directional Reflectance Distribution Functions (SVBRDFs). Learning‐based approaches for appearance capture will play a key role in the development of new technologies that will exhibit a significant impact on virtually all domains of graphics. Therefore, to facilitate future research, this State‐of‐the‐Art Report (STAR) presents an in‐depth overview of the state‐of‐the‐art in machine‐learning‐driven material capture in general, and focuses on SVBRDF acquisition in particular, due to its importance in accurately modelling complex light interaction properties of real‐world materials. The overview includes a categorization of current methods along with a summary of each technique, an evaluation of their functionalities, their complexity in terms of acquisition requirements, computational aspects and usability constraints. The STAR is concluded by looking forward and summarizing open challenges in research and development toward predictive and general appearance capture in this field. A complete list of the methods and papers reviewed in this survey is available at .en_US
dc.description.number6
dc.description.sectionheadersMajor Revision from Eurographics Conference
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume43
dc.identifier.doi10.1111/cgf.15199
dc.identifier.pages39 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.15199
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf15199
dc.publisher© 2024 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectmodelling
dc.subjectappearance modelling
dc.subjectrendering
dc.titleDeep SVBRDF Acquisition and Modelling: A Surveyen_US
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