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Mesh-based learning is one of the popular approaches nowadays to learnshapes. The most established backbone in this field is MeshCNN. In this paper,we propose infusing MeshCNN with geometric reasoning to achieve higher qualitylearning. Through careful analysis of the way geometry is representedthrough-out the network, we submit that this representation should be rigidmotion invariant, and should allow reconstructing the original geometry.Accordingly, we introduce the first and second fundamental forms as anedge-centric, rotation and translation invariant, reconstructablerepresentation. In addition, we update the originally proposed pooling schemeto be more geometrically driven. We validate our analysis throughexperimentation, and present consistent improvement upon the MeshCNN baseline,as well as other more elaborate state-of-the-art architectures. Furthermore, wedemonstrate this fundamental forms-based representation opens the door toaccessible generative machine learning over meshes.

MeshCNN Fundamentals: Geometric Learning through a Reconstructable Representation