  We present a novel approach for generating isotropic surface triangle meshes
directly from unoriented 3D point clouds, with the mesh density adapting to the
estimated local feature size (LFS). Popular reconstruction pipelines first
reconstruct a dense mesh from the input point cloud and then apply remeshing to
obtain an isotropic mesh. The sequential pipeline makes it hard to find a
lower-density mesh while preserving more details. Instead, our approach
reconstructs both an implicit function and an LFS-aware mesh sizing function
directly from the input point cloud, which is then used to produce the final
LFS-aware mesh without remeshing. We combine local curvature radius and shape
diameter to estimate the LFS directly from the input point clouds.
Additionally, we propose a new mesh solver to solve an implicit function whose
zero level set delineates the surface without requiring normal orientation. The
added value of our approach is generating isotropic meshes directly from 3D
point clouds with an LFS-aware density, thus achieving a trade-off between
geometric detail and mesh complexity. Our experiments also demonstrate the
robustness of our method to noise, outliers, and missing data and can preserve
sharp features for CAD point clouds.
