Review for NeurIPS paper: Neural Unsigned Distance Fields for Implicit Function Learning

Summary and Contributions: Paper proposes an approach to produce un-signed distance field as a 3D shape representation for input sparse point cloud. This approach facilitate training on 3D dataset for which water tight meshes are hard to generate. Furthermore, this approach is also applicable for general curve, surface and manifold (spiral) approximation. The approach is simple and general, which promises wider applicability. Update: Authors have provided more experimental results on shapenet and comparison with SAL.