  Reconstructing continuous surfaces from 3D point clouds is a fundamental
operation in 3D geometry processing. Several recent state-of-the-art methods
address this problem using neural networks to learn signed distance functions
(SDFs). In this paper, we introduce \textit{Neural-Pull}, a new approach that
is simple and leads to high quality SDFs. Specifically, we train a neural
network to pull query 3D locations to their closest points on the surface using
the predicted signed distance values and the gradient at the query locations,
both of which are computed by the network itself. The pulling operation moves
each query location with a stride given by the distance predicted by the
network. Based on the sign of the distance, this may move the query location
along or against the direction of the gradient of the SDF. This is a
differentiable operation that allows us to update the signed distance value and
the gradient simultaneously during training. Our outperforming results under
widely used benchmarks demonstrate that we can learn SDFs more accurately and
flexibly for surface reconstruction and single image reconstruction than the
state-of-the-art methods.
