  Retrieving range information in three-dimensional (3D) radio imaging is
particularly challenging due to the limited communication bandwidth and pilot
resources. To address this issue, we consider a reconfigurable intelligent
surface (RIS)-aided uplink communication scenario, generating multiple
measurements through RIS phase adjustment. This study successfully realizes 3D
single-frequency imaging by exploiting the near-field multi-view image
correlations deduced from user mobility. We first highlight the significance of
considering anisotropy in multi-view image formation by investigating radar
cross-section properties and diffraction resolution limits. We then propose a
novel model for joint multi-view 3D imaging that incorporates occlusion effects
and anisotropic scattering. These factors lead to slow image support variation
and smooth coefficient evolution, which are mathematically modeled as Markov
processes. Based on this model, we employ the Expectation
Maximization-Turbo-Generalized Approximate Message Passing algorithm for joint
multi-view single-frequency 3D imaging with limited measurements. Simulation
results reveal the superiority of joint multi-view imaging in terms of enhanced
imaging ranges, accuracies, and anisotropy characterization compared to
single-view imaging. Combining adjacent observations for joint multi-view
imaging enables a reduction in the measurement overhead by 80%.
