  Both uncertainty-assisted and iteration-based methods have achieved great
success in stereo matching. However, existing uncertainty estimation methods
take a single image and the corresponding disparity as input, which imposes
higher demands on the estimation network. In this paper, we propose Cost
volume-based disparity Uncertainty Estimation (UEC). Based on the rich
similarity information in the cost volume coming from the image pairs, the
proposed UEC can achieve competitive performance with low computational cost.
Secondly, we propose two methods of uncertainty-assisted disparity estimation,
Uncertainty-based Disparity Rectification (UDR) and Uncertainty-based Disparity
update Conditioning (UDC). These two methods optimise the disparity update
process of the iterative-based approach without adding extra parameters. In
addition, we propose Disparity Rectification loss that significantly improves
the accuracy of small amount of disparity updates. We present a
high-performance stereo architecture, DR Stereo, which is a combination of the
proposed methods. Experimental results from SceneFlow, KITTI, Middlebury 2014,
and ETH3D show that DR-Stereo achieves very competitive disparity estimation
performance.
