InStereo2K: a large real dataset for stereo matching in indoor scenes

Deep neural networks have shown great success in stereo matching in recent years. On the KITTI datasets, most top performing methods are based on neural networks. However, on the Middlebury datasets, these methods usually do not perform well. The KITTI datasets are collected in outdoor scenes while the Middlebury datasets are collected in indoor scenes. It is commonly believed that the community still lacks a large labelled dataset for stereo matching in indoor scenes. In this paper, we introduce a new stereo dataset called InStereo2K. It contains 2050 pairs of stereo images with highly accurate groundtruth disparity maps, including 2000 pairs for training and 50 pairs for test. Experimental results show that our dataset can significantly improve the performance of several latest networks (including StereoNet and PSMNet) on the Middlebury 2014 dataset. The large scale, high accuracy and rich diversity of the proposed InStereo2K dataset provide new opportunities to researchers in the area of stereo matching and beyond. It also takes end-to-end stereo matching methods a step towards practical applications.