  Representing shapes as level sets of neural networks has been recently proved
to be useful for different shape analysis and reconstruction tasks. So far,
such representations were computed using either: (i) pre-computed implicit
shape representations; or (ii) loss functions explicitly defined over the
neural level sets. In this paper we offer a new paradigm for computing high
fidelity implicit neural representations directly from raw data (i.e., point
clouds, with or without normal information). We observe that a rather simple
loss function, encouraging the neural network to vanish on the input point
cloud and to have a unit norm gradient, possesses an implicit geometric
regularization property that favors smooth and natural zero level set surfaces,
avoiding bad zero-loss solutions. We provide a theoretical analysis of this
property for the linear case, and show that, in practice, our method leads to
state of the art implicit neural representations with higher level-of-details
and fidelity compared to previous methods.
