Mesh defiltering via cascaded geometry recovery

M. WEI, X. GUO, J. HUANG, H. XIE*, H. ZONG, R. KWAN, F. L. WANG, J. QIN

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)591-605
Number of pages15
JournalComputer Graphics Forum
Volume38
Issue number7
DOIs
Publication statusPublished - Oct 2019
Externally publishedYes

Bibliographical note

This work was supported by the grants from the National Natural Science Foundation of China (No. 61502137), the Top‐Up Fund (TFG‐04) and Seed Fund (SFG‐10) for General Research Fund/Early Career Scheme, the Interdisciplinary Research Scheme of the Dean's Research Fund 2018‐19 (FLASS/DRF/IDS‐3), the Departmental Collaborative Research Fund 2019 (MIT/DCRF‐R2/18‐19), the Funding Support to General Research Fund Proposal (RG 39/2019‐2020R), the Internal Research Grant (RG 90/2018‐2019R) of The Education University of Hong Kong, and The Hong Kong Polytechnic University (No. G‐YBZE).

Keywords

  • CCS Concepts
  • Computing methodologies → Shape analysis

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