  Millimeter Wave Radar is being adopted as a viable alternative to lidar and
radar in adverse visually degraded conditions, such as the presence of fog and
dust. However, this sensor modality suffers from severe sparsity and noise
under nominal conditions, which makes it difficult to use in precise
applications such as mapping. This work presents a novel solution to generate
accurate 3D maps from sparse radar point clouds. RMap uses a custom generative
transformer architecture, UpPoinTr, which upsamples, denoises, and fills the
incomplete radar maps to resemble lidar maps. We test this method on the
ColoRadar dataset to demonstrate its efficacy.
