FlyingThings3D is a synthetic dataset for optical flow, disparity and scene flow estimation. It consists of everyday objects flying along randomized 3D trajectories. We generated about 25,000 stereo frames with ground truth data. Instead of focusing on a particular task (like KITTI) or enforcing strict naturalism (like Sintel), we rely on randomness and a large pool of rendering assets to generate orders of magnitude more data than any existing option, without running a risk of repetition or saturation.
The Middlebury 2014 dataset contains a set of 23 high resolution stereo pairs for which known camera calibration parameters and ground truth disparity maps obtained with a structured light scanner are available. The images in the Middlebury dataset all show static indoor scenes with varying difficulties including repetitive structures, occlusions, wiry objects as well as untextured areas. Source: Using Self-Contradiction to Learn Confidence Measures in Stereo Vision Image Source: https://vision.middlebury.edu/stereo/data/scenes2014/
IRS is an open dataset for indoor robotics vision tasks, especially disparity and surface normal estimation. It contains totally 103,316 samples covering a wide range of indoor scenes, such as home, office, store and restaurant. Source: https://github.com/HKBU-HPML/IRS Image Source: https://github.com/HKBU-HPML/IRS
Dynamic Replica is a synthetic dataset of stereo videos featuring humans and animals in virtual environments. It is a benchmark for dynamic disparity/depth estimation and 3D reconstruction consisting of 145,200 stereo frames (524 videos). The dataset contains annotations for left and right views that include: camera intrinsics and extrinsics, image depth, instance segmentation masks, binary foreground / background segmentation masks, optical flow, long-range pixel trajectories