Interferometric inverse synthetic aperture radar (InISAR) can achieve 3-D imageries of maneuvering target. However, the quality of 3-D imageries suffers from the noise and sidelobes seriously. In addition, the defocusing effect due to the nonlinear moving of the target can also bring into a bad effect for 3-D imaging. To tackle these issues, a 3-D InISAR imaging approach via compressed sensing (CS)-based super-resolution (SR) ISAR imageries is proposed. First, we establish the target sparsity-constraint optimization model containing both amplitude and phase information of scatterers. Second, a Bayesian CS approach is adopted to get high-quality SR ISAR image. The quasi-Newton solver guarantees both high amplitude and phase information recovery precision. At last, a high-quality 3-D view of the target is achieved via the conventional ISAR imagery pair interferometry technique. Computer simulation and real data experimental results verify the effectiveness of the proposed method.