FADNet++: Real-Time and Accurate Disparity Estimation with Configurable Networks

Understand

Deep neural networks (DNNs) have achieved great success in the area of computer vision.

• The disparity estimation problem tends to be addressed by DNNs which achieve much better prediction accuracy than traditional hand-crafted feature-based methods.
• However, the existing DNNs hardly serve both efficient computation and rich expression capability, which makes them difficult for deployment in real-time and high-quality applications, especially on mobile devices.
• To this end, we propose an efficient, accurate, and configurable deep network for disparity estimation named FADNet++.