  Estimating a mesh from an unordered set of sparse, noisy 3D points is a
challenging problem that requires carefully selected priors. Existing
hand-crafted priors, such as smoothness regularizers, impose an undesirable
trade-off between attenuating noise and preserving local detail. Recent
deep-learning approaches produce impressive results by learning priors directly
from the data. However, the priors are learned at the object level, which makes
these algorithms class-specific and even sensitive to the pose of the object.
We introduce meshlets, small patches of mesh that we use to learn local shape
priors. Meshlets act as a dictionary of local features and thus allow to use
learned priors to reconstruct object meshes in any pose and from unseen
classes, even when the noise is large and the samples sparse.
