SVBRDF Reconstruction by Transferring Lighting Knowledge

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dc.contributor.authorZhu, Pengfeien_US
dc.contributor.authorLai, Shuichangen_US
dc.contributor.authorChen, Mufanen_US
dc.contributor.authorGuo, Jieen_US
dc.contributor.authorLiu, Yifanen_US
dc.contributor.authorGuo, Yanwenen_US
dc.contributor.editorChaine, Raphaëlleen_US
dc.contributor.editorDeng, Zhigangen_US
dc.contributor.editorKim, Min H.en_US
dc.date.accessioned2023-10-09T07:37:02Z
dc.date.available2023-10-09T07:37:02Z
dc.date.issued2023
dc.description.abstractThe problem of reconstructing spatially-varying BRDFs from RGB images has been studied for decades. Researchers found themselves in a dilemma: opting for either higher quality with the inconvenience of camera and light calibration, or greater convenience at the expense of compromised quality without complex setups. We address this challenge by introducing a twobranch network to learn the lighting effects in images. The two branches, referred to as Light-known and Light-aware, diverge in their need for light information. The Light-aware branch is guided by the Light-known branch to acquire the knowledge of discerning light effects and surface reflectance properties, but without the reliance of light positions. Both branches are trained using the synthetic dataset, but during testing on real-world cases without calibration, only the Light-aware branch is activated. To facilitate a more effective utilization of various light conditions, we employ gated recurrent units (GRUs) to fuse the features extracted from different images. The two modules mutually benefit when multiple inputs are provided. We present our reconstructed results on both synthetic and real-world examples, demonstrating high quality while maintaining a lightweight characteristic in comparison to previous methods.en_US
dc.description.number7
dc.description.sectionheadersLearning-based Reflectance
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume42
dc.identifier.doi10.1111/cgf.14973
dc.identifier.issn1467-8659
dc.identifier.pages11 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.14973
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14973
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Computing methodologies -> Reflectance modeling
dc.subjectComputing methodologies
dc.subjectReflectance modeling
dc.titleSVBRDF Reconstruction by Transferring Lighting Knowledgeen_US
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