Direct Elastic Unrollings of Painted Pottery Surfaces from Sparse Image Sets

Abstract

An important task in archaeological research is the comparison of painted motifs on ancient vessels and the analysis of their painting style. Ideally, the pottery objects are available as scanned 3D models, from which the painted surface can be unrolled and potential distortions minimized, so that the vase painting and its individual motifs can be directly inspected. Unfortunately, the percentage of digitally captured vessels is small compared to the large body of cataloged photographs. In this paper, we present a method that creates distortion-minimized unrollings of painted pottery surfaces directly from a small set of photographs. We achieve this by exploiting prior knowledge about the data, namely that most objects exhibit rotational symmetry and that strict guidelines were followed when capturing photographs of the ancient vases. Based on the distinctly visible object silhouettes in the photographs we are able to extract proxy geometries of the objects which we encode as per-view geometric maps. By stitching the single-view data, we obtain a combined map capturing the geometry and texture of the entire painted surface. This enables us to minimize typical projective distortions by elastic relaxation. Our pipeline works entirely in 2D image space, circumventing time-consuming 3D scans and surface reconstructions of (often inaccessible) vessels. Using a combination of CPU-based image processing and GPU-based relaxation, results are produced in only a few minutes.