  Neural implicit fields have recently emerged as a useful representation for
3D shapes. These fields are commonly represented as neural networks which map
latent descriptors and 3D coordinates to implicit function values. The latent
descriptor of a neural field acts as a deformation handle for the 3D shape it
represents. Thus, smoothness with respect to this descriptor is paramount for
performing shape-editing operations. In this work, we introduce a novel
regularization designed to encourage smooth latent spaces in neural fields by
penalizing the upper bound on the field's Lipschitz constant. Compared with
prior Lipschitz regularized networks, ours is computationally fast, can be
implemented in four lines of code, and requires minimal hyperparameter tuning
for geometric applications. We demonstrate the effectiveness of our approach on
shape interpolation and extrapolation as well as partial shape reconstruction
from 3D point clouds, showing both qualitative and quantitative improvements
over existing state-of-the-art and non-regularized baselines.
