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Mesh Defiltering via Cascaded Geometry Recovery
Author(s) -
Wei M.,
Guo X.,
Huang J.,
Xie H.,
Zong H.,
Kwan R.,
Wang F. L.,
Qin J.
Publication year - 2019
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13863
Subject(s) - polygon mesh , computer science , ground truth , geometry processing , filter (signal processing) , algorithm , function (biology) , volume mesh , set (abstract data type) , mesh generation , artificial intelligence , geometry , computer vision , mathematics , finite element method , computer graphics (images) , physics , evolutionary biology , biology , programming language , thermodynamics
Abstract This 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.