A disparity map is a key component of stereo vision systems. Autonomous navigation, 3D reconstruction and mobility are examples of areas that use disparity maps as an important element. Although much work has been done in the stereo vision field, it is not easy to build stereo systems with concepts such as reuse and extensible scope. In the present paper, we contribute to reducing this gap by presenting a software architecture that can accommodate different stereo methods through a new standard structure. Firstly, we introduce scenarios that illustrate use cases of disparity maps, and we show a novel architecture that foments code reuse. A Disparity Computation Framework (DCF) is presented and how its components are structured regarding compartmentalization are discussed. Then, we introduce a prototype that closely follows our proposal, and we describe some test cases that were performed. We conclude that the DCF can satisfy different on-demand scenarios and that it can support new stereo methods, functions, and evaluations for different applications without much effort.