CageNet: A Meta-Framework for Learning on Wild Meshes
This work addresses a domain-specific problem for researchers and practitioners in computer graphics and geometry processing who need to handle imperfect mesh data.
The paper tackles the problem of applying generic learning frameworks to 'wild' meshes with multiple components, non-manifold elements, or disrupted connectivity, by proposing CageNet, a configurable meta-framework based on caged geometry, which achieves better performance than state-of-the-art techniques on tasks like segmentation and skinning weights.
Learning on triangle meshes has recently proven to be instrumental to a myriad of tasks, from shape classification, to segmentation, to deformation and animation, to mention just a few. While some of these applications are tackled through neural network architectures which are tailored to the application at hand, many others use generic frameworks for triangle meshes where the only customization required is the modification of the input features and the loss function. Our goal in this paper is to broaden the applicability of these generic frameworks to "wild", i.e. meshes in-the-wild which often have multiple components, non-manifold elements, disrupted connectivity, or a combination of these. We propose a configurable meta-framework based on the concept of caged geometry: Given a mesh, a cage is a single component manifold triangle mesh that envelopes it closely. Generalized barycentric coordinates map between functions on the cage, and functions on the mesh, allowing us to learn and test on a variety of data, in different applications. We demonstrate this concept by learning segmentation and skinning weights on difficult data, achieving better performance to state of the art techniques on wild meshes.