Restoring artworks seriously damaged or completely destroyed is a challenging task. In particular, the reconstruction of frescoes has to deal with problems such as very small fragments, irregular shapes and missing pieces. Several attempts have been done to develop new techniques for helping restorers in the matching process, starting from traditional image processing methods to the more recent deep learning approaches. However, as often happens in the Cultural Heritage field, the availability of labeled data to test new strategies is limited, and publicly available datasets contain only few samples. For this reason, in this paper we introduce DAFNE, a large dataset that includes hundreds of thousands of images of fresco fragments artificially generated to guarantee a high variability in terms of shapes and dimensions. Fragments have been obtained starting from 62 images of famous frescoes of various artists and historical periods, in order to consider different artistic styles, subjects and colors.

恢复严重损坏或完全毁坏的艺术品是一项艰巨的任务。特别地，壁画的重建必须处理诸如非常小的碎片，不规则形状和碎片丢失的问题。从传统的图像处理方法到最新的深度学习方法，已经进行了数种尝试来开发新技术来帮助修复人员进行匹配过程。但是，正如在文化遗产领域中经常发生的那样，用于测试新策略的标记数据的可用性是有限的，可公开获得的数据集仅包含少量样本。因此，在本文中，我们介绍了DAFNE，这是一个大型数据集，其中包含数十万幅人工生成的壁画片段图像，以确保形状和尺寸方面的高度可变性。