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. In this work, the authors provide a statistical characterization and setup used for the generation of two datasets about boulders on small bodies that are made publicly available.
@article{arxiv.2210.16253,
title = {DOORS: Dataset fOr bOuldeRs Segmentation. Statistical properties and Blender setup},
author = {Mattia Pugliatti and Francesco Topputo},
journal= {arXiv preprint arXiv:2210.16253},
year = {2022}
}