  In this paper, we explore the feasibility of utilizing a mmWave radar sensor
installed on a UAV to reconstruct the 3D shapes of multiple objects in a space.
The UAV hovers at various locations in the space, and its onboard radar senor
collects raw radar data via scanning the space with Synthetic Aperture Radar
(SAR) operation. The radar data is sent to a deep neural network model, which
outputs the point cloud reconstruction of the multiple objects in the space. We
evaluate two different models. Model 1 is our recently proposed 3DRIMR/R2P
model, and Model 2 is formed by adding a segmentation stage in the processing
pipeline of Model 1. Our experiments have demonstrated that both models are
promising in solving the multiple object reconstruction problem. We also show
that Model 2, despite producing denser and smoother point clouds, can lead to
higher reconstruction loss or even loss of objects. In addition, we find that
both models are robust to the highly noisy radar data obtained by unstable SAR
operation due to the instability or vibration of a small UAV hovering at its
intended scanning point. Our exploratory study has shown a promising direction
of applying mmWave radar sensing in 3D object reconstruction.
