Dual Geometric Graph Network (DG2N) Iterative Network for Deformable Shape Alignment

Dvir Ginzburg, Dan Raviv

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

Abstract

We provide a novel approach for aligning geometric models using a dual graph structure where local features are mapping probabilities. Alignment of non-rigid structures is one of the most challenging computer vision tasks due to the high number of unknowns needed to model the correspondence. We have seen a leap forward using DNN models in template alignment and functional maps,but those methods fail for inter-class alignment where non-isometric deformations exist. Here we propose to rethink this task and use unrolling concepts on a dual graph structure - one for a forward map and one for a backward map,where the features are pulled back matching probabilities from the target into the source. We report state of the art results on stretchable domains' alignment in a rapid and stable solution for meshes and point clouds.

Original languageEnglish
Title of host publicationProceedings - 2021 International Conference on 3D Vision, 3DV 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1341-1350
Number of pages10
ISBN (Electronic)9781665426886
DOIs
StatePublished - 2021
Event9th International Conference on 3D Vision, 3DV 2021 - Virtual, Online, United Kingdom
Duration: 1 Dec 20213 Dec 2021

Publication series

NameProceedings - 2021 International Conference on 3D Vision, 3DV 2021

Conference

Conference9th International Conference on 3D Vision, 3DV 2021
Country/TerritoryUnited Kingdom
CityVirtual, Online
Period1/12/213/12/21

Keywords

  • Deep learning
  • dense correspondence refinment
  • dense shape correspondence
  • iterative deep learning

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