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Multi-scale similarity enhanced guided normal filtering

  • Wenbo Zhao*
  • , Xianming Liu
  • , Shiqi Wang
  • , Debin Zhao
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we propose a novel mesh denoising scheme in which multi-scale similarity is exploited to improve the performance of non-local normal filtering for feature-preserved mesh restoration. In our scheme, K-ring patches are used to identify multi-scale local structures around faces, and we compare the similarity between patches on multiple levels. The multi-scale similarities are subsequently computed by weighted similarity of patches. Finally, the center faces of similar patches are weighted by similarities in face normal filtering. Experimental results on different models indicate that the proposed method outperforms other local and non-scale-aware similarity based schemes in terms of both objective and subjective evaluations.

Original languageEnglish
Title of host publicationAdvances in Multimedia Information Processing – PCM 2017 - 18th Pacific-Rim Conference on Multimedia, Revised Selected Papers
EditorsBing Zeng, Hongliang Li, Qingming Huang, Abdulmotaleb El Saddik, Shuqiang Jiang, Xiaopeng Fan
PublisherSpringer Verlag
Pages645-653
Number of pages9
ISBN (Print)9783319773827
DOIs
StatePublished - 2018
Externally publishedYes
Event18th Pacific-Rim Conference on Multimedia, PCM 2017 - Harbin, China
Duration: 28 Sep 201729 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10736 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th Pacific-Rim Conference on Multimedia, PCM 2017
Country/TerritoryChina
CityHarbin
Period28/09/1729/09/17

Keywords

  • Face normal filtering
  • Feature-preserved
  • K-ring patch
  • Mesh denoising
  • Multi-scale similarity

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