Realistic CG Stereo Image Dataset with Ground Truth Disparity Maps

Stereo matching is one of the most active research areas in computer vision. While a large number of algorithms for stereo correspondence have been developed, research in some branches of the field has been constrained due to the few number of stereo datasets with ground truth disparity maps available. Having available a large dataset of stereo images with ground truth disparity maps would boost the research on new stereo matching methods, for example, methods based on machine learning. In this work we develop a large stereo dataset with ground truth disparity maps using highly realistic computer graphic techniques. We also apply some of the most common stereo matching techniques to our dataset to confirm that our highly realistic CG stereo images remain as challenging as real-world stereo images. This dataset will also be of great use for camera tracking algorithms, because we provide the exact camera position and rotation in every frame.