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Millimeter wave radars can perceive through occlusions like dust, fog, smoke and clothes. But compared to cameras and lidars, their perception quality is orders of magnitude poorer. RadarHD [3] tackles this problem of poor quality by creating a machine learning super resolution pipeline trained against high quality lidar scans to mimic lidar. RadarHD ingests low resolution radar and generates high quality lidar-like point clouds even in occluded settings. RadarHD can also make use of the high quality output for typical robotics tasks like odometry, mapping and classification using conventional lidar workflows. Here, we demonstrate the effectiveness of RadarHD's point clouds against lidar in occluded settings.