弯曲图：使用门控最优传输的分层形状匹配
Bending Graphs: Hierarchical Shape Matching using Gated Optimal Transport
摘要：
形状匹配一直是计算机图形学和视觉界长期研究的问题。目标是预测具有一定程度变形的网格之间的密集对应。现有的方法要么考虑采样点的局部描述，要么基于全局形状信息发现对应关系。在这项工作中，我们研究了一个分层的学习设计，我们将当地的补丁级信息和全球形状级结构。这种灵活的表示使得能够进行对应预测，并为匹配阶段提供丰富的特征。最后，我们提出了一种新的最优传输求解器，通过循环更新非置信节点上的特征来学习形状之间的全局一致对应。我们在公开数据集上的结果表明，在存在严重变形的情况下，无需进行大量的训练或改进，就可以实现强大的性能。
英文摘要：
Shape matching has been a long-studied problem for the computer graphics and vision community. The objective is to predict a dense correspondence between meshes that have a certain degree of deformation. Existing methods either consider the local description of sampled points or discover correspondences based on global shape information. In this work, we investigate a hierarchical learning design, to which we incorporate local patch-level information and global shape-level structures. This flexible representation enables correspondence prediction and provides rich features for the matching stage. Finally, we propose a novel optimal transport solver by recurrently updating features on non-confident nodes to learn globally consistent correspondences between the shapes. Our results on publicly available datasets suggest robust performance in presence of severe deformations without the need for extensive training or refinement.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
