  A neural implicit outputs a number indicating whether the given query point
in space is inside, outside, or on a surface. Many prior works have focused on
_latent-encoded_ neural implicits, where a latent vector encoding of a specific
shape is also fed as input. While affording latent-space interpolation, this
comes at the cost of reconstruction accuracy for any _single_ shape. Training a
specific network for each 3D shape, a _weight-encoded_ neural implicit may
forgo the latent vector and focus reconstruction accuracy on the details of a
single shape. While previously considered as an intermediary representation for
3D scanning tasks or as a toy-problem leading up to latent-encoding tasks,
weight-encoded neural implicits have not yet been taken seriously as a 3D shape
representation. In this paper, we establish that weight-encoded neural
implicits meet the criteria of a first-class 3D shape representation. We
introduce a suite of technical contributions to improve reconstruction
accuracy, convergence, and robustness when learning the signed distance field
induced by a polygonal mesh -- the _de facto_ standard representation. Viewed
as a lossy compression, our conversion outperforms standard techniques from
geometry processing. Compared to previous latent- and weight-encoded neural
implicits we demonstrate superior robustness, scalability, and performance.
