  Recently, neural networks have been used as implicit representations for
surface reconstruction, modelling, learning, and generation. So far, training
neural networks to be implicit representations of surfaces required training
data sampled from a ground-truth signed implicit functions such as signed
distance or occupancy functions, which are notoriously hard to compute.
  In this paper we introduce Sign Agnostic Learning (SAL), a deep learning
approach for learning implicit shape representations directly from raw,
unsigned geometric data, such as point clouds and triangle soups.
  We have tested SAL on the challenging problem of surface reconstruction from
an un-oriented point cloud, as well as end-to-end human shape space learning
directly from raw scans dataset, and achieved state of the art reconstructions
compared to current approaches. We believe SAL opens the door to many geometric
deep learning applications with real-world data, alleviating the usual
painstaking, often manual pre-process.
