a) Decimated Mesh (b) MeshNet Point Coverage (c) MeshNet++ Point CoverageFigure 1: Robustness to decimation by MeshNet++; (a) Mesh Decimated to 1024 faces; (b) Points captured by MeshNet while generating structural and spatial features; (c) Points captured by MeshNet++ while generating only structural features. It is evident that MeshNet++ captures the original shape of a mesh and is robust to the shortcomings of mesh decimation.

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Unlike images, meshes are irregular and unstructured. Thus, it is not trivial to extend existing image-based deep learning approaches for mesh analysis. In this paper, inspired by dilated convolutions for images, we proffer dilated convolutions for meshes. Our Dilated Mesh Convolution (DMC) unit inflates the kernels' receptive field without increasing the number of learnable parameters. We also propose a Stacked Dilated Mesh Convolution (SDMC) block by stacking DMC units. It considers spatial regions around mesh faces' at multiple scales while summarizing the neighboring contextual information. We accommodated SDMC in MeshNet to classify 3D meshes. Experimental results demonstrate that this redesigned model significantly improves classification accuracy on multiple data sets. Code

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