论文标题
双几何图网络（DG2N） - 可变形形状比对的迭代网络
Dual Geometric Graph Network (DG2N) -- Iterative network for deformable shape alignment
论文作者
论文摘要
我们提供了一种新型的新方法，用于使用局部特征是映射概率的双图结构来对齐几何模型。由于对信件建模所需的大量未知数，因此非刚性结构的对齐是最具挑战性的计算机视觉任务之一。我们已经在模板对齐和功能图中使用DNN模型看到了一个飞跃，但是这些方法无法在存在非等法变形的情况下进行类间比对。在这里，我们建议重新考虑这项任务，并在双图结构上使用展开的概念 - 一个用于向前映射，一个用于向后映射，其中将功能从目标中拉回匹配的概率从目标到源。我们在快速稳定的解决方案和点云中，在可伸缩的域对齐中报告了最新的结果。
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.
