![]() ![]() Another important feature is the scale of the dataset. The images were extracted from video files with 15 fps at HD2K resolution with a size of 2280 × 1282 pixels. The frames show different paths from the perspective of a pedestrian, including sidewalks, trails, roads, etc. 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. two global shutter sensors for streaming up to 90 Pictures/Sec.SYNTHIA 2 or The SYNTHetic collection of Imagery and Annotations consists of a collection of photo-realistic frames rendered from a virtual city.For integration into applications, numerous prepared and very well documented interfaces are already available, which support, among others, ROS, Unity, OpenCV, PCL and many more. A development environment in C/C++, Python, Nodejs & C# is also available. ![]() The Intel RealSense SDK 2.0 is available for Windows, Linux and macOS, and enables the visualization of the measurement/image data within minutes. This strategy makes the Intel RealSense product range unique and equally interesting for users in different industries. In addition, the cameras are divided into two categories, for the prototype status, the Intel RealSense is supplied with a tripod & USB cable, whereby no ESD protection or similar needs to be taken into account - However, only the pure image sensor, without peripherals & housing, can also be purchased afterwards, which can be installed in the own system. As a result, these 3D cameras (depth cameras) can be integrated into the desired application within a very short time and enable machine vision. The Intel RealSense product range impresses with very good documentation paired with a small form factor. ![]()
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