  Learning-based surface reconstruction based on unsigned distance functions
(UDF) has many advantages such as handling open surfaces. We propose SuperUDF,
a self-supervised UDF learning which exploits a learned geometry prior for
efficient training and a novel regularization for robustness to sparse
sampling. The core idea of SuperUDF draws inspiration from the classical
surface approximation operator of locally optimal projection (LOP). The key
insight is that if the UDF is estimated correctly, the 3D points should be
locally projected onto the underlying surface following the gradient of the
UDF. Based on that, a number of inductive biases on UDF geometry and a
pre-learned geometry prior are devised to learn UDF estimation efficiently. A
novel regularization loss is proposed to make SuperUDF robust to sparse
sampling. Furthermore, we also contribute a learning-based mesh extraction from
the estimated UDFs. Extensive evaluations demonstrate that SuperUDF outperforms
the state of the arts on several public datasets in terms of both quality and
efficiency. Code url is https://github.com/THHHomas/SuperUDF.
