  We introduce neural dual contouring (NDC), a new data-driven approach to mesh
reconstruction based on dual contouring (DC). Like traditional DC, it produces
exactly one vertex per grid cell and one quad for each grid edge intersection,
a natural and efficient structure for reproducing sharp features. However,
rather than computing vertex locations and edge crossings with hand-crafted
functions that depend directly on difficult-to-obtain surface gradients, NDC
uses a neural network to predict them. As a result, NDC can be trained to
produce meshes from signed or unsigned distance fields, binary voxel grids, or
point clouds (with or without normals); and it can produce open surfaces in
cases where the input represents a sheet or partial surface. During experiments
with five prominent datasets, we find that NDC, when trained on one of the
datasets, generalizes well to the others. Furthermore, NDC provides better
surface reconstruction accuracy, feature preservation, output complexity,
triangle quality, and inference time in comparison to previous learned (e.g.,
neural marching cubes, convolutional occupancy networks) and traditional (e.g.,
Poisson) methods. Code and data are available at
https://github.com/czq142857/NDC.
