  Neural implicits have become popular for representing surfaces because they
offer an adaptive resolution and support arbitrary topologies. While previous
works rely on ground truth point clouds, they often ignore the effect of input
quality and sampling methods during reconstructing process. In this paper, we
introduce a sampling method with an uncertainty-augmented surface implicit
representation that employs a sampling technique that considers the geometric
characteristics of inputs. To this end, we introduce a strategy that
efficiently computes differentiable geometric features, namely, mean
curvatures, to augment the sampling phase during the training period. The
uncertainty augmentation offers insights into the occupancy and reliability of
the output signed distance value, thereby expanding representation capabilities
into open surfaces. Finally, we demonstrate that our method leads to
state-of-the-art reconstructions on both synthetic and real-world data.
