  Millimetre-wave (mmWave) radars can generate 3D point clouds to represent
objects in the scene. However, the accuracy and density of the generated point
cloud can be lower than a laser sensor. Although researchers have used mmWave
radars for various applications, there are few quantitative evaluations on the
quality of the point cloud generated by the radar and there is a lack of a
standard on how this quality can be assessed. This work aims to fill the gap in
the literature. A radar simulator is built to evaluate the most common data
processing chains of 3D point cloud construction and to examine the capability
of the mmWave radar as a 3D imaging sensor under various factors. It will be
shown that the radar detection can be noisy and have an imbalance distribution.
To address the problem, a novel super-resolution point cloud construction
(SRPC) algorithm is proposed to improve the spatial resolution of the point
cloud and is shown to be able to produce a more natural point cloud and reduce
outliers.
