Anastylosis is an archaeological technique which focuses on the reconstruction of collapsed building and destroyed artworks, starting from the original pieces. Many digital approaches have been developed in the last decade, mainly based on 2D and 3D analysis of the structure of the fragments. These techniques aim at supporting the priceless work of the involved operators, mainly in the decision processes and in the resolution of positioning ambiguities. Techniques acting with this scope lie in the field of the digital anastylosis. In this paper we present SAFFO, a digital approach to 2D reconstruction of frescoes, based on the extraction of SIFT features from a painting. The approach appears to be very robust to false positives, resulting optimal in scenarios involving fragment sets containing spurious elements. The experiments have been performed on the DAFNE (Digital Anastylosis for Fresco challeNgE) dataset, which gathers more than 30 2D artworks and provides several tessellation for each. For its robustness against spurious fragments, SAFFO won the third place in the rank list of DAFNE Challenge 2019.

吻合术是一项考古技术，其重点是从原始作品开始重建倒塌的建筑物和被破坏的艺术品。在过去的十年中，主要基于片段结构的2D和3D分析，开发了许多数字方法。这些技术旨在支持相关运营商的无价工作，主要是在决策过程和解决定位歧义方面。具有此作用域的技术属于数字吻合术领域。在本文中，我们基于从绘画中提取SIFT特征，提出了SAFFO，这是一种数字化的壁画2D重建方法。该方法对于误报似乎非常健壮，因此在涉及包含伪元素的片段集的场景中是最佳的。实验是在DAFNE（用于Fresco challeNgE的数字吻合术）数据集上进行的，该数据集收集了30多个2D艺术品，并为每个艺术品提供了多个细分。SAFFO凭借其对虚假碎片的鲁棒性，在DAFNE Challenge 2019的排名中排名第三。