Complex multidimensional capturing setups such as plenoptic cameras (PC) introduce a trade-off between various system properties. Consequently, established capturing properties, like image resolution, need to be described thoroughly for these systems. Therefore models and metrics that assist exploring and formulating this trade-off are highly beneficial for studying as well as designing of complex capturing systems. This work demonstrates the capability of our previously proposed sampling pattern cube (SPC) model to extract the lateral resolution for plenoptic capturing systems. The SPC carries both ray information as well as focal properties of the capturing system it models. The proposed operator extracts the lateral resolution from the SPC model throughout an arbitrary number of depth planes giving a depth-resolution profile. This operator utilizes focal properties of the capturing system as well as the geometrical distribution of the light containers which are the elements in the SPC model. We have validated the lateral resolution operator for different capturing setups by comparing the results with those from Monte Carlo numerical simulations based on the wave optics model. The lateral resolution predicted by the SPC model agrees with the results from the more complex wave optics model better than both the ray based model and our previously proposed lateral resolution operator. This agreement strengthens the conclusion that the SPC fills the gap between ray-based models and the real system performance, by including the focal information of the system as a model parameter. The SPC is proven a simple yet efficient model for extracting the lateral resolution as a high-level property of complex plenoptic capturing systems.