A Generative and Entropy-Based Registration Approach for the Reassembly of Ancient Inscriptions

Abstract

An increased interest in computer-aided heritage reconstruction has emerged in recent years due to the maturity of sophisticated computer vision techniques. Concretely, feature-based matching methods have been conducted to reassemble heritage assets, yielding plausible results for data that contains enough salient points for matching. However, they fail to register ancient artifacts that have been badly deteriorated over the years. In particular, for monochromatic incomplete data, such as 3D sunk relief eroded decorations, damaged drawings, and ancient inscriptions. The main issue lies in the lack of regions of interest and poor quality of the data, which prevent feature-based algorithms from estimating distinctive descriptors. This paper addresses the reassembly of damaged decorations by deploying a Generative Adversarial Network (GAN) to predict the continuing decoration traces of broken heritage fragments. By extending the texture information of broken counterpart fragments, it is demonstrated that registration methods are now able to find mutual characteristics that allow for accurate optimal rigid transformation estimation for fragments alignment. This work steps away from feature-based approaches, hence employing Mutual Information (MI) as a similarity metric to estimate an alignment transformation. Moreover, high-resolution geometry and imagery are combined to cope with the fragility and severe damage of heritage fragments. Therefore, the testing data is composed of a set of ancient Egyptian decorated broken fragments recorded through 3D remote sensing techniques. More specifically, structured light technology for mesh models creation, as well as orthophotos, upon which digital drawings are created. Even though this study is restricted to Egyptian artifacts, the workflow can be applied to reconstruct different types of decoration patterns in the cultural heritage domain.

Keywords

• digital heritage reassembling
• generative adversarial networks
• mutual information registration