TY - JOUR
T1 - Cascaded Normal Filtering Neural Network for Geometry-Aware Mesh Denoising of Measurement Surfaces
AU - Zhu, Dingkun
AU - Zhang, Yingkui
AU - Li, Zhiqi
AU - Wang, Weiming
AU - Xie, Haoran
AU - Wei, Mingqiang
AU - Cheng, Gary
AU - Wang, Fu Lee
N1 - Publisher Copyright:
© 1963-2012 IEEE.
PY - 2021
Y1 - 2021
N2 - Mesh denoising is a fundamental component of many disparate reverse engineering applications of measurement surfaces. This article presents a cascaded normal filtering neural network (termed a CNF-Net) for geometry-aware mesh denoising of measurement surfaces. CNF-Net leverages the geometry domain knowledge (GDK) that, a mesh approximates to its underlying surface compactly if all mesh facets at most lie on the surface intersections while not crossing them. Benefiting from the well-estimated underlying geometry of noisy mesh facets, a multiscale guidedly filtered normal descriptor (M-GFND) is formulated, and multiple height maps are constructed from the M-GFND. The height maps can be effectively fed into CNF-Net for learning the transformation matrices between the M-GFND and the ground-truth facet normal. CNF-Net can automatically handle meshes with multiscale geometric features yet corrupted by the noise of different distributions, while existing learning-based wisdoms commonly pursue an overall normal estimation accuracy yet fail to preserve surface significant features. Both visual and numerical evaluations on synthetic and real noise data sets consistently show the clear improvements of CNF-Net over the state-of-the-arts.
AB - Mesh denoising is a fundamental component of many disparate reverse engineering applications of measurement surfaces. This article presents a cascaded normal filtering neural network (termed a CNF-Net) for geometry-aware mesh denoising of measurement surfaces. CNF-Net leverages the geometry domain knowledge (GDK) that, a mesh approximates to its underlying surface compactly if all mesh facets at most lie on the surface intersections while not crossing them. Benefiting from the well-estimated underlying geometry of noisy mesh facets, a multiscale guidedly filtered normal descriptor (M-GFND) is formulated, and multiple height maps are constructed from the M-GFND. The height maps can be effectively fed into CNF-Net for learning the transformation matrices between the M-GFND and the ground-truth facet normal. CNF-Net can automatically handle meshes with multiscale geometric features yet corrupted by the noise of different distributions, while existing learning-based wisdoms commonly pursue an overall normal estimation accuracy yet fail to preserve surface significant features. Both visual and numerical evaluations on synthetic and real noise data sets consistently show the clear improvements of CNF-Net over the state-of-the-arts.
KW - Cascaded normal filtering neural network (CNF-Net)
KW - geometry domain knowledge (GDK)
KW - measurement surface
KW - mesh denoising
KW - neural network
KW - normal filtering
UR - http://www.scopus.com/inward/record.url?scp=85101763303&partnerID=8YFLogxK
U2 - 10.1109/TIM.2021.3061247
DO - 10.1109/TIM.2021.3061247
M3 - Article
AN - SCOPUS:85101763303
SN - 0018-9456
VL - 70
JO - IEEE Transactions on Instrumentation and Measurement
JF - IEEE Transactions on Instrumentation and Measurement
M1 - 9360624
ER -