论文标题
G-MSM：与基于图的亲和力先验的无监督多形匹配
G-MSM: Unsupervised Multi-Shape Matching with Graph-based Affinity Priors
论文作者
论文摘要
我们提供G-MSM（基于图的多形匹配），这是一种针对非刚性形状对应的新型无监督学习方法。我们没有将输入集合作为无序的样本集，而是明确对基础形状数据歧管进行建模。为此，我们提出了一种自适应的多形匹配体系结构，该体系结构以自我监督的方式在给定的一组训练形状上构建亲和力图。关键思想是通过沿着基础形状图中的最短路径传播图来结合推定的成对对应关系。在训练过程中，我们在此类最佳路径和成对匹配之间实现了周期矛盾，这使我们的模型能够学习拓扑感知的形状先验。我们探索不同类别的形状图并恢复特定的设置，例如基于模板的匹配（星形图）或可学习的排名/排序（TSP图），作为我们框架中的特殊情况。最后，我们在最近的几个形状信号基准上展示了最先进的性能，包括带有拓扑噪声和具有挑战性的阶层对的现实世界3D扫描网眼。
We present G-MSM (Graph-based Multi-Shape Matching), a novel unsupervised learning approach for non-rigid shape correspondence. Rather than treating a collection of input poses as an unordered set of samples, we explicitly model the underlying shape data manifold. To this end, we propose an adaptive multi-shape matching architecture that constructs an affinity graph on a given set of training shapes in a self-supervised manner. The key idea is to combine putative, pairwise correspondences by propagating maps along shortest paths in the underlying shape graph. During training, we enforce cycle-consistency between such optimal paths and the pairwise matches which enables our model to learn topology-aware shape priors. We explore different classes of shape graphs and recover specific settings, like template-based matching (star graph) or learnable ranking/sorting (TSP graph), as special cases in our framework. Finally, we demonstrate state-of-the-art performance on several recent shape correspondence benchmarks, including real-world 3D scan meshes with topological noise and challenging inter-class pairs.
