  Recent deep learning based approaches have outperformed classical stereo
matching methods. However, current deep learning based end-to-end stereo
matching methods adopt a generic encoder-decoder style network with skip
connections. To limit computational requirement, many networks perform
excessive down sampling, which results in significant loss of useful low-level
information. Additionally, many network designs do not exploit the rich
multi-scale contextual information. In this work, we address these
aforementioned problems by carefully designing the network architecture to
preserve required spatial information throughout the network, while at the same
time achieve large effective receptive field to extract multiscale contextual
information. For the first time, we create a synthetic disparity dataset
reflecting real life images captured using a smartphone; this enables us to
obtain state-of-the-art results on common real life images. The proposed model
DISCO is pre-trained on the synthetic Scene Flow dataset and evaluated on
popular benchmarks and our in-house dataset of challenging real life images.
The proposed model outperforms existing state-of-the-art methods in terms of
quality as well as quantitative metrics.
