TY - JOUR
T1 - Mesh denoising guided by patch normal co-filtering via kernel low-rank recovery
AU - Wei, Mingqiang
AU - Huang, Jin
AU - Xie, Xingyu
AU - Liu, Ligang
AU - Wang, Jun
AU - Qin, Jing
N1 - Funding Information:
The authors would like to thank the anonymous reviewers for their valuable comments. This work was supported by the grants from the National Natural Science Foundation of China (No. 61502137, No. 61772267, No. 61672482), the Fundamental Research Funds for the Central Universities (NE2016004), the One Hundred Talent Project of the Chinese Academy of Sciences, the Innovation and Technology Fund of Hong Kong (No. ITS/026/17), the NUAA Fundamental Research Funds (NS2015053), the China Postdoctoral Science Foundation (No. 2016M592047), the State Key Lab. for Novel Software Technology, Nanjing University (No. KFKT2018B20), and the Shenzhen Science and Technology Program (No. JCYJ20170413162617606).
Publisher Copyright:
© 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
PY - 2019/8/11
Y1 - 2019/8/11
N2 - Mesh denoising is a classical, yet not well-solved problem in digital geometry processing. The challenge arises from noise removal with the minimal disturbance of surface intrinsic properties (e.g., sharp features and shallow details). We propose a new patch normal co-filter (PcFilter) for mesh denoising. It is inspired by the geometry statistics which show that surface patches with similar intrinsic properties exist on the underlying surface of a noisy mesh. We model the PcFilter as a low-rank matrix recovery problem of similar-patch collaboration, aiming at removing different levels of noise, yet preserving various surface features. We generalize our model to pursue the low-rank matrix recovery in the kernel space for handling the nonlinear structure contained in the data. By making use of the block coordinate descent minimization and the specifics of a proximal based coordinate descent method, we optimize the nonlinear and nonconvex objective function efficiently. The detailed quantitative and qualitative results on synthetic and real data show that the PcFilter competes favorably with the state-of-the-art methods in surface accuracy and noise-robustness.
AB - Mesh denoising is a classical, yet not well-solved problem in digital geometry processing. The challenge arises from noise removal with the minimal disturbance of surface intrinsic properties (e.g., sharp features and shallow details). We propose a new patch normal co-filter (PcFilter) for mesh denoising. It is inspired by the geometry statistics which show that surface patches with similar intrinsic properties exist on the underlying surface of a noisy mesh. We model the PcFilter as a low-rank matrix recovery problem of similar-patch collaboration, aiming at removing different levels of noise, yet preserving various surface features. We generalize our model to pursue the low-rank matrix recovery in the kernel space for handling the nonlinear structure contained in the data. By making use of the block coordinate descent minimization and the specifics of a proximal based coordinate descent method, we optimize the nonlinear and nonconvex objective function efficiently. The detailed quantitative and qualitative results on synthetic and real data show that the PcFilter competes favorably with the state-of-the-art methods in surface accuracy and noise-robustness.
KW - Kernel low-rank recovery
KW - Patch normal co-filtering
KW - Self-similarity
KW - Terms—Mesh denoising
UR - http://www.scopus.com/inward/record.url?scp=85051621857&partnerID=8YFLogxK
U2 - 10.1109/TVCG.2018.2865363
DO - 10.1109/TVCG.2018.2865363
M3 - Journal article
C2 - 30106734
AN - SCOPUS:85051621857
SN - 1077-2626
VL - 25
SP - 2910
EP - 2926
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
IS - 10
M1 - 2865363
ER -