@inproceedings{8b90fac330974a4480f66fbb1ab20058,
title = "Dual Geometric Graph Network (DG2N) Iterative Network for Deformable Shape Alignment",
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.",
keywords = "Deep learning, dense correspondence refinment, dense shape correspondence, iterative deep learning",
author = "Dvir Ginzburg and Dan Raviv",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 9th International Conference on 3D Vision, 3DV 2021 ; Conference date: 01-12-2021 Through 03-12-2021",
year = "2021",
doi = "https://doi.org/10.1109/3DV53792.2021.00141",
language = "الإنجليزيّة",
series = "Proceedings - 2021 International Conference on 3D Vision, 3DV 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1341--1350",
booktitle = "Proceedings - 2021 International Conference on 3D Vision, 3DV 2021",
address = "الولايات المتّحدة",
}