MeshNet++: A Network with a Face

Polygon meshes are a popular representation in computer graphics. They efficiently provide delineations of complex 3D shapes. However, their irregular structure hinders mesh analysis efforts in deep learning frameworks; few neural networks exist to describe meshes. MeshNet is a pioneer in this direction. In this paper, we propose a novel neural network that is substantially deeper than its MeshNet predecessor. This increase in depth is achieved through our specialized convolution and pooling blocks that operate on mesh faces. Our network named MeshNet++ learns local structures at multiple scales and is also robust to shortcomings of mesh decimation. We evaluated it for the shape classification task on various data sets, and results significantly higher than state-of-the-art were observed. In particular, results demonstrated that even a small number of examples suffice for training MeshNet++. Our code is available at https://github.com/VimsLab/MeshNet2.