  The aim of this paper is the reconstruction of a smooth surface from an
unorganized point cloud sampled by a closed surface, with the preservation of
geometric shapes, without any further information other than the point cloud.
Implicit neural representations (INRs) have recently emerged as a promising
approach to surface reconstruction. However, the reconstruction quality of
existing methods relies on ground truth implicit function values or surface
normal vectors. In this paper, we show that proper supervision of partial
differential equations and fundamental properties of differential vector fields
are sufficient to robustly reconstruct high-quality surfaces. We cast the
$p$-Poisson equation to learn a signed distance function (SDF) and the
reconstructed surface is implicitly represented by the zero-level set of the
SDF. For efficient training, we develop a variable splitting structure by
introducing a gradient of the SDF as an auxiliary variable and impose the
$p$-Poisson equation directly on the auxiliary variable as a hard constraint.
Based on the curl-free property of the gradient field, we impose a curl-free
constraint on the auxiliary variable, which leads to a more faithful
reconstruction. Experiments on standard benchmark datasets show that the
proposed INR provides a superior and robust reconstruction. The code is
available at \url{https://github.com/Yebbi/PINC}.
