Spectral Teacher for a Spatial Student: Spectrum-Aware Real-Time Dense Shape Correspondence

We propose a novel spectral-teacher spatial-student (STS) learning paradigm for non-rigid dense shape correspondence. Current methods can be segmented into two categories; Spectral where the Laplace Beltrami Operator self-functions are used as a relevant basis, and Spatial where the actual coordinates are used directly in the input channel. Today state-of-the-art reported results were provided by spectral methods, as global and local schema interact. Unfortunately, these methods suffer from numerical instability, and are not real-time, so they are irrelevant for some modalities or applications. On the other hand, spatial methods are fast for inference but lack the global view and report inferior results. Here, for the first time, we show that all you need is a good teacher to improve the spatial self-supervised models. We show that a spectral teacher can provide a spatial student with a deep understanding of the model and significantly improve known real-time alignment schemas. We report superior results by a large margin on FAUST and SHREC'19 databases compared to real-time methods. Our code is publicly available 1.