![]() It was created at the University of Alicante and consists of an RGB-D stereo dataset, which provides 33 different scenes, each with between 2 k and 10 k frames. The dataset presented in this paper is UASOL 8: A Large-scale High-resolution Outdoor Stereo Dataset. It only contains 534 frames, so the scale of this dataset is the smallest of all those reviewed. The different scenes provide human interaction and also different types of paths and roads a pedestrian could use. In addition, the amount of data is not enough to correctly train a more complex deep learning algorithm.įinally, the Make3D dataset 3 is outdoor and taken from the perspective of a pedestrian. The Middlebury dataset provides different lighting conditions for each scene, but as mentioned, theseare indoor static scenes. ![]() All of the scenes provided are indoor, mainly focused on objects. The Middlebury Dataset 7 provides 33 scenes, each filmed from two different exposures. They provide only 6 scenes with complete rooms, which are not ordinary scenes and therefore could not provide good generalizability to the trained models. It only provides static scenes with no interaction, which leads to the scenes provided are being mostly objects. The ground-truth data was captured using an industrial laser scanner which adds precision to the data. Tanks and Temples 6 includes 147791 RGB-D frames in 14 different scenes. Of the 25 scenes, only 9 are outdoor which significantly decreases the number of images. The ground-truth was taken with a highly accurate 3D laser scanner. The ETH3D dataset 4 includes 534 RGB-D frames divided into 25 scenes. The only problem of this dataset is that it was captured from the perspective of a car, so the main view is from the road. This dataset is outdoor, so it fulfills one of our main requisites. The KITTI dataset 5 provides the RGB (stereo pair) and depth maps of 400 different layouts having a total of 1.6 k frames of roads from the city of Karlsruhe (Germany). The second problem is that the dataset is centered on the vision of a car driving in the street, while, in our case, we need the point of view of the pedestrians. The first is that it is a synthetic dataset, meaning that the frames are not photorealistic, with the subsequent problem of testing the system in real conditions. There are two main problems with this dataset. If the ZED is recognized by your computer, you'll see the 3D video from your camera.SYNTHIA 2 or The SYNTHetic collection of Imagery and Annotations consists of a collection of photo-realistic frames rendered from a virtual city. It lets you change video resolution, aspect ratio, camera parameters, and capture high resolution snapshots and 3D video. The ZED Explorer ( /usr/local/zed/tools/ZED Explorer ) is an application for ZED live preview and recording. ![]() Select your platform and follow the installation guide: Linux, Jetson,Windows.Ĭurrently, we offer a companion SDK with Ubuntu version 18.04. The ZED SDK is available for Windows, Linux and Nvidia Jetson platforms.But I personally recommend using Ubuntu under Linux.It contains all the libraries that powers your camera along with tools that let you test its features and settings. ZED cameras are all UVC compliant so they should be automatically recognized by your computer. Unpack your camera, plug the camera in a USB 3.0 port and go to the next step. 1 Zed Quick Started guide 1.1 Connect your camera ![]()
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