Figure 1: Our acquisition system, deployed at the Akrotiri Excavation, Thera. We use a flatbed scanner to capture high-resolution images and normals of wall painting fragments (shown at left), and multiple 3-D scanners to acquire geometry. A single user can operate up to four scanners simultaneously, while a second user operates the flatbed scanner and verifies processing results. This yields a throughput of approximately 10 fragments per hour. Our matching algorithm correctly finds the only two matches in this data set using the scanned 3-D geometry. AbstractAlthough mature technologies exist for acquiring images, geometry, and normals of small objects, they remain cumbersome and time-consuming for non-experts to employ on a large scale. In an archaeological setting, a practical acquisition system for routine use on every artifact and fragment would open new possibilities for archiving, analysis, and dissemination. We present an inexpensive system for acquiring all three types of information, and associated metadata, for small objects such as fragments of wall paintings. The acquisition system requires minimal supervision, so that a single, non-expert user can scan at least 10 fragments per hour. To achieve this performance, we introduce new algorithms to robustly and automatically align range scans, register 2-D scans to 3-D geometry, and compute normals from 2-D scans. As an illustrative application, we present a novel 3-D matching algorithm that efficiently searches for matching fragments using the scanned geometry.

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In this article, we analyze the fracture patterns observed in wall paintings excavated at Akrotiri, a Bronze Age Aegean settlement destroyed by a volcano on the Greek island of Thera around 1630 BC. We use interactive programs to trace detailed fragment boundaries in images of manually reconstructed wall paintings. Then, we use geometric analysis algorithms to study the shapes and contacts of those fragment boundaries, producing statistical distributions of lengths, angles, areas, and adjacencies found in assembled paintings. The result is a statistical model that suggests a hierarchical fracture pattern where fragments break into two pieces recursively along cracks nearly orthogonal to previous ones. This model is tested by comparing it with simulation results of a hierarchical fracture process. The model could be useful for predicting fracture patterns of other wall paintings and/or for guiding future computer-assisted reconstruction algorithms.

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