The vast majority of archaeological artefacts are discovered in a fragmentary state, and the poor state of preservation of these pieces further hampers the extent of the archaeological research possible. Moreover, related pieces of historical importance and interest may be dispersed across different collections making the study of their relationships or, in special cases, their reassembly difficult and even impossible.
Much effort and time is expended in trying to re-associate across collections pieces which share a common history, reunify pieces that were once part of the same artefact or reassemble these eroded and damaged fragments as accurately and completely as possible, whether for public display or historical research.
Recent technology developments in the area of 3D scanning and digital shape analysis and matching have shown that it is possible to scan recently fractured surfaces and reassemble them using visualisation tools and geometric matching. However, most of the applications which are encountered in the reconstruction of cultural heritage objects call for non-exact matching techniques, since surfaces are generally heavily abraded or damaged, and exact or near-exact matches are not possible. GRAVITATE aims to address this issue through a combination of geometrical and semantic approaches to discover similarity and likelihood of match. In addition to the development of improved matching algorithms more suited to this specific task, the project partners are working together to create a pipeline specifically targeted at the similarity assessments of 3D artefacts found in the real world, concurrently evaluating heterogeneous properties such as geometric aspects (e.g. curvature, size, roundness or mass distribution), photometric aspects (e.g. texture, colour distribution or surface patterning) and semantic annotation which includes contextual knowledge added by curators.
With the improved technical capability being developed by GRAVITATE, we will be able to go far beyond the manual or semi-manual matching techniques currently used. Moreover, by adding explicit semantic components to our understanding of the parts of artefacts we will enable researchers to discover matches and similarities that may not have been obvious in the past.
