Indirect imaging correlography (IIC) is a coherent imaging technique that provides access to the autocorrelation of the albedo of objects obscured from line-of-sight. This technique is used to recover sub-mm resolution images of obscured objects at large standoffs in non-line-of-sight (NLOS) imaging. However, predicting the exact resolving power of IIC in any given NLOS scene is complicated by the interplay between several factors, including object position and pose. This work puts forth a mathematical model for the imaging operator in IIC to accurately predict the images of objects in NLOS imaging scenes. Using the imaging operator, expressions for the spatial resolution as a function of scene parameters such as object position and pose are derived and validated experimentally. In addition, a self-supervised deep neural network framework to reconstruct images of objects from their autocorrelation is proposed. Using this framework, objects with ≈ 250 μ m features, located at 1 mt standoffs in an NLOS scene, are successfully reconstructed.