Automating the reassembly of valuable broken relics is imperative to mitigate the risks associated with manual handling. This paper introduces a solution leveraging computer vision for 3D data acquisition to extract features from high-dimensional data, focusing on the automatic reassembly of broken glass relics. The proposed approach begins by digitizing the broken shards and extracting and segmenting their contours. Subsequently, the system maps these segments into a manifold space to assess similarity in local geometry using Local Tangent Space Alignment and identify pairwise matches among them. A global optimization step based on Minimum Spanning Tree then determines the overall solution of the reassembly problem, aligning the shards to visualize a digitally reassembled relic. This digital solution is deployed in an application for head-mounted augmented reality devices, guiding users through the sequential reconstruction of the real relic. Experimental validation across ten manually fractured glass relics shows robust matching accuracy, with alignment success rates over 90% and processing times averaging one hour per object. Additionally, the system’s performance is assessed in scenarios involving missing shards, demonstrating the robustness in matching shards but encountering challenges in aligning shards around absent pieces.
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