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:
this https URL

提出了一种基于学习的方法，用于计算非刚性三维形状之间的对应关系。该方法利用从原始形状几何中直接学习的特征提取网络，结合一种基于功能映射表示的正则化地图提取层和损失函数，能够从比现有的监督方法少的训练数据中学习，并且比当前基于描述符学习的方法更加普适。