3D Recording and Point Cloud Analysis for Detecting and Tracking Morphological Deterioration in Archaeological Metals

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Abstract

Cultural Heritage documentation field is living an outstanding technological renovation, and techniques as image processing and computer vision are becoming a more and more valuable resource for its preservation, research and diffusion. Trying to contribute in this field, we present a line of research focused on determining how precision three-dimensional records can help us to detect and quantify formal changes in archaeological metals. This interest is marked by the fact that this heritage usually presents advanced stages of corrosion that imply material fragility and make them susceptible to suffer physical damage in a short period of time. Our experimental context was the temporary loan of a rusted iron helmet. Its state of preservation was recorded before and after it was being transferred, supporting us in two systems of great diffusion in the field of 3D digitalization of heritage: structured light 3D imaging and Structure from Motion (SfM) systems. Finally, the M3C2 technique was used to quantify and interpret the geometric differences between these records and assess the significance of their accuracy.

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Fuentes-Porto, A., Díaz-Aleman, D., & Díaz-González, E. (2020). 3D Recording and Point Cloud Analysis for Detecting and Tracking Morphological Deterioration in Archaeological Metals. In Learning and Analytics in Intelligent Systems (Vol. 7, pp. 362–367). Springer Nature. https://doi.org/10.1007/978-3-030-36778-7_40

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