We present Learning Delaunay Surface Elements for Mesh Reconstruction (DSE meshing), for any point in an input point cloud,
we select the k-nearest neighbors and extract the subset of points that are in the geodesic neighborhood of the center point,
using a learned classification network. A projection network then estimates a log map projection of the points into a 2D embedding,
where we can apply Delaunay Triangulation to get a DSE.
We show qualitative comparison of our DSE meshing to state of the art methods below.
We evaluate our method on non uniformly sampled point clouds. Shapes are sampled more densely to the left and more coarsely to the right. We can see that methods struggle to reconstruct the coarsely sampled parts of the point cloud. While our method also has slightly more errors in the coarsely sampled regions, the mesh quality drops by a much smaller amount from densely to coarsely sampled regions.