The UnrealDataset is a self-collected synthetic dataset that comprises of more than 100k images and 21 sequences collected in a bunch of highly photorealistic urban and forest scenarios with Unreal Engine and the AirSim plugin, which allows us to navigate a simulated MAV inside any Unreal scenarios. The plugin also allows us to collect MAV’s frontal camera RGB images, ground truth depth up to 40 meters and segmentation labels.



Training set

Test set

In each sequence's directory you will find:

  1. RGB images
  2. Depth ground truth: 8-bit PNG format. Here you can find the pseudocode for conversion in metric values 
  3. Segmentation ground truth:two classes, "obstacle"(pixel set to 1) and "not-obstacle"(pixel set to 0)
  4. Obstacles_20m: txt files containing ground truth labels for obstacles with a depth up to 20 meters. It follows the object parametrization introduced here. The image is divided in a 8x5 grid of 32x32 cells. Each obstacle belongs to the cell containing the center of its minimum enclosing bounding box. Each line defines an obstacle and it can be read as follows:

    [x-index of the cell, y-index of the cell, x coordinate of the bounding box center, y coordinates of the bounding box center, bounding box width, bounding box height, average mean of the obstacle depth, variance of the obstacle depth]

    The coordinates of the bounding box center are parameterized with respect of the cell and are normalized between [0,1]. Width and height are normalized between [0,1] with respect of the size of the image. The mean and the variance are normalized between [0,1] with respect of the maximum measured depth, which is 39,75 m. 


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