Dual Geometric Graph Network (DG2N) -- Iterative network for deformable shape alignment

• URL: http://arxiv.org/abs/2011.14723v2
• Date: Sat, 27 Mar 2021 06:23:32 GMT
• Title: Dual Geometric Graph Network (DG2N) -- Iterative network for deformable shape alignment
• Authors: Dvir Ginzburg and Dan Raviv
• Abstract summary: We provide a novel new approach for aligning geometric models using a dual graph structure where local features are mapping probabilities. We report state of the art results on stretchable domains alignment in a rapid and stable solution for meshes and cloud of points.
• Score: 8.325327265120283
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We provide a novel new 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 nonisometric 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 cloud of points.

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