论文标题
深度几何功能图：形状对应的鲁棒特征学习
Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence
论文作者
论文摘要
我们提出了一种基于学习的新方法，用于计算非刚性3D形状之间的对应关系。与以前需要大量培训数据或在手工制作的输入描述符上运行的方法不同，因此在各种数据集中概括的方法不当，我们的方法既准确又适合形状结构的变化。我们方法的关键是一个功能萃取网络，该网络可直接从原始形状的几何形状中学习，并基于功能图表示，并结合了新型的正则地图提取层和丢失。我们通过在挑战性匹配方案中进行的广泛实验来证明，我们的方法可以从培训数据中学习比现有监督方法更少的学习数据，并且比当前基于描述符的学习方法更明显地概括了。我们的源代码可在以下网址提供：https：//github.com/lix-shape-analysis/geomfmaps。
We present a novel learning-based approach for computing correspondences between non-rigid 3D shapes. Unlike previous methods that either require extensive training data or operate on handcrafted input descriptors and thus generalize poorly across diverse datasets, our approach is both accurate and robust to changes in shape structure. Key to our method is a feature-extraction network that learns directly from raw shape geometry, combined with a novel regularized map extraction layer and loss, based on the functional map representation. We demonstrate through extensive experiments in challenging shape matching scenarios that our method can learn from less training data than existing supervised approaches and generalizes significantly better than current descriptor-based learning methods. Our source code is available at: https://github.com/LIX-shape-analysis/GeomFmaps.
