Towards a simulation driven stereo vision system

This paper presents a novel algorithm for estimating stereo disparity which exploits the benefit of learning to the fullest. Given a cost volume of stereo matching, we solve the cost aggregation and disparity computation in one shot by using a classifier; we design a feature called matching cost pattern for the input which we extract from the cost volume while we use simulated stereo patterns for training. To this end, we introduce a highly realistic computer graphics dataset, the new Tsukuba stereo dataset, with ground-truth disparity maps. Through preliminary experiments we show that our algorithm outperforms a simplified AD-Census cost-minimization method, and also that the error ratio decreases as we use a larger number of samples for training.