Virtual KITTI is a photo-realistic synthetic video dataset designed to learn and evaluate computer vision models for several video understanding tasks: object detection and multi-object tracking, scene-level and instance-level semantic segmentation, optical flow, and depth estimation.
99 PAPERS • 1 BENCHMARK
ETHD is a multi-view stereo benchmark / 3D reconstruction benchmark that covers a variety of indoor and outdoor scenes. Ground truth geometry has been obtained using a high-precision laser scanner. A DSLR camera as well as a synchronized multi-camera rig with varying field-of-view was used to capture images.
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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.
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DrivingStereo contains over 180k images covering a diverse set of driving scenarios, which is hundreds of times larger than the KITTI Stereo dataset. High-quality labels of disparity are produced by a model-guided filtering strategy from multi-frame LiDAR points.
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Virtual KITTI 2 is an updated version of the well-known Virtual KITTI dataset which consists of 5 sequence clones from the KITTI tracking benchmark. In addition, the dataset provides different variants of these sequences such as modified weather conditions (e.g. fog, rain) or modified camera configurations (e.g. rotated by 15◦). For each sequence we provide multiple sets of images containing RGB, depth, class segmentation, instance segmentation, flow, and scene flow data. Camera parameters and poses as well as vehicle locations are available as well. In order to showcase some of the dataset’s capabilities, we ran multiple relevant experiments using state-of-the-art algorithms from the field of autonomous driving. The dataset is available for download at https://europe.naverlabs.com/Research/Computer-Vision/Proxy-Virtual-Worlds.
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Dataset provided by the Image Matching Workshop
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A dataset consisting of stereo thermal, stereo color, and cross-modality image pairs with high accuracy ground truth (< 2mm) generated from a LiDAR. The authors scanned 100 cluttered indoor and 80 outdoor scenes featuring challenging environments and conditions. CATS contains approximately 1400 images of pedestrians, vehicles, electronics, and other thermally interesting objects in different environmental conditions, including nighttime, daytime, and foggy scenes.
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The Middlebury 2006 is a stereo dataset of indoor scenes with multiple handcrafted layouts.
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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.
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PST900 is a dataset of 894 synchronized and calibrated RGB and Thermal image pairs with per pixel human annotations across four distinct classes from the DARPA Subterranean Challenge.
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The Middlebury 2001 is a stereo dataset of indoor scenes with multiple handcrafted layouts.
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The UASOL an RGB-D stereo dataset, that contains 160902 frames, filmed at 33 different scenes, each with between 2 k and 10 k frames. The frames show different paths from the perspective of a pedestrian, including sidewalks, trails, roads, etc. The images were extracted from video files with 15 fps at HD2K resolution with a size of 2280 × 1282 pixels. The dataset also provides a GPS geolocalization tag for each second of the sequences and reflects different climatological conditions. It also involved up to 4 different persons filming the dataset at different moments of the day.
3 PAPERS • 1 BENCHMARK