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In this paper, we explore the feasibility of utilizing a mmWave radar sensorinstalled 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 senorcollects raw radar data via scanning the space with Synthetic Aperture Radar(SAR) operation. The radar data is sent to a deep neural network model, whichoutputs the point cloud reconstruction of the multiple objects in the space. Weevaluate two different models. Model 1 is our recently proposed 3DRIMR/R2Pmodel, and Model 2 is formed by adding a segmentation stage in the processingpipeline of Model 1. Our experiments have demonstrated that both models arepromising in solving the multiple object reconstruction problem. We also showthat Model 2, despite producing denser and smoother point clouds, can lead tohigher reconstruction loss or even loss of objects. In addition, we find thatboth models are robust to the highly noisy radar data obtained by unstable SARoperation due to the instability or vibration of a small UAV hovering at itsintended scanning point. Our exploratory study has shown a promising directionof applying mmWave radar sensing in 3D object reconstruction.

3D Reconstruction of Multiple Objects by mmWave Radar on UAV