Capturing Anisotropic SVBRDFs

dc.contributor.authorKaltheuner, Julianen_US
dc.contributor.authorBode, Lukasen_US
dc.contributor.authorKlein, Reinharden_US
dc.contributor.editorAndres, Bjoern and Campen, Marcel and Sedlmair, Michaelen_US
dc.date.accessioned2021-09-25T16:36:23Z
dc.date.available2021-09-25T16:36:23Z
dc.date.issued2021
dc.description.abstractIn this work, we adapt and improve recent isotropic material estimation efforts to estimate spatially varying anisotropic materials with an additional Fresnel term using a variable set of input images and are able to handle any resolution. We combine an initial estimation network with an auto-encoder to fine-tune the decoding of latent embedded appearance parameters on the input images to produce finely detailed SVBRDFs. For this purpose, the training must be adapted so that the determination is possible on the basis of a small number of images that still capture as much reflective behavior of materials as possible. The resulting appearance parameters are capable of capturing and reconstructing complex spatially varying features in detail, but place increased demands on the input images.en_US
dc.description.sectionheadersCapturing and Rendering
dc.description.seriesinformationVision, Modeling, and Visualization
dc.identifier.doi10.2312/vmv.20211372
dc.identifier.isbn978-3-03868-161-8
dc.identifier.pages63-70
dc.identifier.urihttps://doi.org/10.2312/vmv.20211372
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vmv20211372
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectReflectance modeling
dc.titleCapturing Anisotropic SVBRDFsen_US
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