Paper Info
Title
Mesh defiltering via cascaded geometry recovery
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.
Year
DOI
Venue
2019
10.1111/cgf.13863
COMPUTER GRAPHICS FORUM
Field
DocType
Volume
Computer vision,Computer graphics (images),Computer science,Artificial intelligence
Journal
38.0
Issue
ISSN
Citations 
7.0
0167-7055
2
PageRank 
References 
Authors
0.35
0
8
Name
Order
Citations
PageRank
Mingqiang Wei112522.66
Xianglin Guo220.69
Jin Huang32910.71
Haoran Xie445071.21
Hua Zong520.35
Reggie Kwan620.69
Fu Lee Wang7926118.55
Jing Qin8110995.43