FanNet: A mesh convolution operator for learning dense maps

In this paper, we introduce a fast, simple and novel mesh convolution operator for learning dense shape correspondences. Instead of calculating weights between nodes, we explicitly aggregate node features by serializing neighboring vertices in a fan-shaped order. Thereafter, we use a fully connected layer to encode vertex features combined with the local neighborhood information. Finally, we feed the resulting features into the multi-resolution functional maps module to acquire the final maps. We demonstrate that our method works well in both supervised and unsupervised settings, and can be applied to isometric shapes with arbitrary triangulation and resolution. We evaluate the proposed method on two widely-used benchmark datasets, FAUST and SCAPE. Our results show that FanNet runs significantly faster and provides on-par or better performance than the related state-of-the-art shape correspondence methods.