Current CNN-based stereo matching methods have demonstrated superior performance compared to traditional stereo matching methods. However, mapping these algorithms into embedded devices, which exhibit limited compute resources, and achieving high performance is a challenging task due to the high computational complexity of the CNN-based methods. The recently proposed StereoNet network, achieves disparity estimation with reduced complexity, whereas performance does not greatly deteriorate. Towards pushing this performance to complexity trade-off further, we propose an optimization applied to StereoNet that adapts the computations to the input data, steering the computations to the regions of the input that would benefit from the application of the CNN-based stereo matching algorithm, where the rest of the input is processed by a traditional, less computationally demanding method. Key to the proposed methodology is the introduction of a lightweight CNN that predicts the importance of r efining a region of the input to the quality of the final disparity map, allowing the system to trade-off computational complexity for disparity error on-demand, enabling the application of these methods to embedded systems with real-time requirements
