  Surface reconstruction with preservation of geometric features is a
challenging computer vision task. Despite significant progress in implicit
shape reconstruction, state-of-the-art mesh extraction methods often produce
aliased, perceptually distorted surfaces and lack scalability to
high-resolution 3D shapes. We present a data-driven approach for automatic
feature detection and remeshing that requires only a coarse, aliased mesh as
input and scales to arbitrary resolution reconstructions. We define and learn a
collection of surface-based fields to (1) capture sharp geometric features in
the shape with an implicit vertexwise model and (2) approximate improvements in
normals alignment obtained by applying edge-flips with an edgewise model. To
support scaling to arbitrary complexity shapes, we learn our fields using local
triangulated patches, fusing estimates on complete surface meshes. Our feature
remeshing algorithm integrates the learned fields as sharp feature priors and
optimizes vertex placement and mesh connectivity for maximum expected surface
improvement. On a challenging collection of high-resolution shape
reconstructions in the ABC dataset, our algorithm improves over
state-of-the-art by 26% normals F-score and 42% perceptual
$\text{RMSE}_{\text{v}}$.
