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dc.contributor.authorWei, Mingqiangen_US
dc.contributor.authorGuo, Xianglinen_US
dc.contributor.authorHuang, Jinen_US
dc.contributor.authorXie, Haoranen_US
dc.contributor.authorZong, Huaen_US
dc.contributor.authorKwan, Reggieen_US
dc.contributor.authorWang, Fu Leeen_US
dc.contributor.authorQin, Jingen_US
dc.contributor.editorLee, Jehee and Theobalt, Christian and Wetzstein, Gordonen_US
dc.description.abstractThis paper addresses the nontraditional but practically meaningful reversibility problem of mesh filtering. This reverse-filtering approach (termed a DeFilter) seeks to recover the geometry of a set of filtered meshes to their artifact-free status. To solve this scenario, we adapt cascaded normal regression (CNR) to understand the commonly used mesh filters and recover automatically the mesh geometry that was lost through various geometric operations. We formulate mesh defiltering by an extreme learning machine (ELM) on the mesh normals at an offline training stage and perform it automatically at a runtime defiltering stage. Specifically, (1) to measure the local geometry of a filtered mesh, we develop a generalized reverse Filtered Facet Normal Descriptor (grFND) in the consistent neighbors; (2) to map the grFNDs to the normals of the ground-truth meshes, we learn a regression function from a set of filtered meshes and their ground-truth counterparts; and (3) at runtime, we reversely filter the normals of a filtered mesh, using the learned regression function for recovering the lost geometry. We evaluate multiple quantitative and qualitative results on synthetic and real data to verify our DeFilter's performance thoroughly. From a practical point of view, our method can recover the lost geometry of denoised meshes without needing to know the exact filter used previously, and can act as a geometry-recovery plugin for most of the state-of-the-art methods of mesh denoising.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies → Shape analysis
dc.titleMesh Defiltering via Cascaded Geometry Recoveryen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersShape Analysis

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  • 38-Issue 7
    Pacific Graphics 2019 - Symposium Proceedings

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