Real-time semi-global dense stereo solution with improved sub-pixel accuracy

In this work we focus on creating a real-time dense stereo reconstruction system with accurate sub-pixel estimation. We selected the Semi-Global Matching method as the basis of our system due to its high quality and possible real-time implementations. In our solution we use the Census transform as the matching metric because our results show that it can reduce the matching errors for traffic images compared to classical solutions. We also propose several modifications to the original Semi-Global algorithm to improve the sub-pixel accuracy and the execution time. One of these proposals is the reduction in the number of optimization directions without affecting the results. The second modification is a correction of the energy function to reduce the spread of depth values. Besides these improvements, the paper also introduces a new aggregation method used to reduce the spread of sub-pixel values. Finally we propose a new method to generate sub-pixel interpolation functions based on real-world data. The result of these enhancements is a significant improvement in sub-pixel accuracy. The system was implemented and evaluated on a current generation GPU with a running time of 19 ms for image having the resolution 512 × 383.