TY - JOUR
T1 - Mesh Defiltering via Cascaded Geometry Recovery
AU - Wei, M.
AU - Guo, X.
AU - Huang, J.
AU - Xie, H.
AU - Zong, H.
AU - Kwan, R.
AU - Wang, F. L.
AU - Qin, J.
N1 - Publisher Copyright:
© 2019 The Author(s) Computer Graphics Forum © 2019 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd.
PY - 2019/10/1
Y1 - 2019/10/1
N2 - 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.
AB - 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.
KW - CCS Concepts
KW - • Computing methodologies → Shape analysis
UR - http://www.scopus.com/inward/record.url?scp=85075173572&partnerID=8YFLogxK
U2 - 10.1111/cgf.13863
DO - 10.1111/cgf.13863
M3 - Article
AN - SCOPUS:85075173572
SN - 0167-7055
VL - 38
SP - 591
EP - 605
JO - Computer Graphics Forum
JF - Computer Graphics Forum
IS - 7
ER -