DPFM: Deep Partial Functional Maps

We consider the problem of computing dense correspondences between non-rigid shapes with potentially significant partiality. Existing formulations tackle this problem through heavy manifold optimization in the spectral domain,given hand-crafted shape descriptors. In this paper,we propose the first learning method aimed directly at partial non-rigid shape correspondence. Our approach uses the functional map framework,can be trained in a supervised or unsupervised manner,and learns descriptors directly from the data,thus both improving robustness and accuracy in challenging cases. Furthermore,unlike existing techniques,our method is also applicable to partial-to-partial non-rigid matching,in which the common regions on both shapes are unknown a priori. We demonstrate that the resulting method is data-efficient,and achieves state-of-the-art results on several benchmark datasets. Our code and data can be found online: https://github.com/pvnieo/DPFM.