DualConv: Dual Mesh Convolutional Networks for Shape Correspondence

Convolutional neural networks have been extremely successful for 2D images and are readily extended to handle 3D voxel data. Meshes are a more common 3D shape representation that quantize the shape surface instead of the ambient space as with voxels, hence giving access to surface properties such as normals or appearances. The formulation of deep neural networks on meshes is, however, more complex since they are irregular data structures where the number of neighbors varies across vertices. While graph convolutional networks have previously been proposed over mesh vertex data, in this paper we explore how these networks can be extended to the dual face-based representation of triangular meshes, where nodes represent triangular faces in place of vertices. In comparison to the primal vertex mesh, its face dual offers several advantages, including, importantly, that the dual mesh is regular in the sense that each triangular face has exactly three neighbors. Moreover, the dual mesh suggests the use of a number of input features that are naturally defined over faces, such as surface normals and face areas. We evaluate the dual approach on the shape correspondence task on the FAUST human shape dataset and other versions of it with varying mesh topology. While applying generic graph convolutions to the dual mesh shows already improvements over primal mesh inputs, our experiments demonstrate that building additionally convolutional models that explicitly leverage the neighborhood size regularity of dual meshes enables learning shape representations that perform on par or better than previous approaches in terms of correspondence accuracy and mean geodesic error, while being more robust to topological changes in the meshes between training and testing shapes.

FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis

Convolutional neural networks (CNNs) have massively impacted visual recognition in 2D images, and are now ubiquitous in state-of-the-art approaches. CNNs do not easily extend, however, to data that are not represented by regular grids, such as 3D shape meshes or other graph-structured data, to which traditional local convolution operators do not directly apply. To address this problem, we propose a novel graph-convolution operator to establish correspondences between filter weights and graph neighborhoods with arbitrary connectivity. The key novelty of our approach is that these correspondences are dynamically computed from features learned by the network, rather than relying on predefined static coordinates over the graph as in previous work. We obtain excellent experimental results that significantly improve over previous state-of-the-art shape correspondence results. This shows that our approach can learn effective shape representations from raw input coordinates, without relying on shape descriptors.