Learning a confidence measure in the disparity domain from O(1) features Abstract   Depth sensing is of paramount importance for countless applications and stereo represents a popular, effective and cheap solution for this purpose. As highlighted by recent works concerned with stereo, uncertainty estimation can be a powerful cue to improve accuracy in stereo. Most confidence measures rely on features, mainly extracted from the cost volume, fed to a random forest or a convolutional neural network trained to estimate match uncertainty. In contrast, we propose a novel strategy for confidence estimation based on features computed in the disparity domain, making our proposal suited for any stereo system including COTS devices, and in constant time. We exhaustively assess the performance of our proposals, referred to as O1 and O2, on KITTI and Middlebury datasets with three popular and different stereo algorithms (CENSUS, MCCNN and SGM), as well as a deep stereo network (PSMNet). We also evaluate how well confidence measures generalize to different environmentsdatasets.

ImageBased EndtoEnd Neural Network for Dense Disparity Estimation Stereo matching is a challenging yet important task to various computer vision applications, e.g. 3D reconstruction, augmented reality, and autonomous vehicles. In this paper, we present a novel imagebased convolutional neural network (CNN) for dense disparity estimation using stereo image pairs. In order to achieve precise and robust stereo matching, we introduce a feature extraction module that learns both local and global information. These features are then passed through an hourglass structure to generate disparity maps from lower resolution to full resolution. We test the proposed method in several datasets including indoor scenes and synthetic scenes. Experimental results demonstrate that the proposed method outperforms the stateoftheart methods in several datasets.