DOORS: Dataset fOr bOuldeRs Segmentation

The capability to detect boulders on the surface of small bodies is beneficial for vision-based applications such as hazard detection during critical operations and navigation. This task is challenging due to the wide assortment of irregular shapes, the characteristics of the boulders population, and the rapid variability in the illumination conditions. Moreover, the lack of publicly available labeled datasets for these applications damps the research about data-driven algorithms. To tackle these challenges, the Dataset fOr bOuldeRs Segmentation (DOORS) has been designed. The dataset is thought to be useful for (but not limited to) boulders recognition, centroid regression, segmentation, and navigation applications. The dataset is divided into two sets:

• Regression: Contains images, masks, and labels for 4 splits of single boulders positioned on the surface of a spherical mesh. It can be used to perform navigation, boulder recognition, segmentation, and centroid regression.
• Segmentation: Contain images, masks, and labels of 2 datasets: DS1 and DS2. DS1 is made of the same images of the Regression dataset but is specifically designed for segmentation. DS2 is made of images with multiple instances of boulders appearing on the surface of the Didymos asteroid model

A detailed characterization of the statistical properties of the DOORS dataset and the description of the Blender setup used to generate it is visible in "DOORS: Dataset fOr bOuldeRs Segmentation. Statistical properties and Blender setup", by Mattia Pugliatti and Francesco Topputo, arXiv pre-print, Oct 2022.