Overfit Neural Networks as a Compact Shape Representation

Neural networks have proven to be effective approximators of signed distance fields (SDFs) for solid 3D objects. While prior work has focused on the generalization power of such approximations, we instead explore their suitability as a compact - if purposefully overfit - SDF representation of individual shapes. Specifically, we ask whether neural networks can serve as first-class implicit shape representations in computer graphics. We call such overfit networks Neural Implicits. Similar to SDFs stored on a regular grid, Neural Implicits have fixed storage profiles and memory layout, but afford far greater accuracy. At equal storage cost, Neural Implicits consistently match or exceed the accuracy of irregularly-sampled triangle meshes. We achieve this with a combination of a novel loss function, sampling strategy and supervision protocol designed to facilitate robust shape overfitting. We demonstrate the flexibility of our representation on a variety of standard rendering and modelling tasks.