An efficient technique to recompose archaeological artifacts from fragments

Sánchez-Belenguer, Carlos; Vendrell-Vidal, Eduardo

doi:10.1109/vsmm.2014.7136671

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…Both [7,8] deal with the reconstruction of broken artifacts by using a 3D acquisition set-up, transforming the objects into point clouds without the aid of neural networks. In particular, the first work aims to find the individual rigid transformations that better align all the fragments.…”

Section: Related Workmentioning

confidence: 99%

“…While creating the framework that allowed us to produce the fragments we kept open the possibility to import solids from an external source. In particular for the experiments in this work, In order to enable us for a common benchmarking ground, we also imported the publicly available 3D scans of some real artifacts, such as the Caligola's Bust 6 and the Nefertiti's Bust 7 . This latter is a scan of an artifact of fundamental importance preserved in the Neues Museum of Berlin (Germany): an old Egyptian bust of Nefertiti.…”

Section: Real Statues Models Dsmentioning

confidence: 99%

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Artificial neural networks exploiting point cloud data for fragmented solid objects classification

Baiocchi,

Giagu,

Napoli

et al. 2023

Mach. Learn.: Sci. Technol.

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This paper presents a novel approach for fragmented solid object classification exploiting neural networks based on point clouds. This work is the initial step of a project in collaboration with the Institution of ‘Ente Parco Archeologico del Colosseo’ in Rome, which aims to reconstruct ancient artifacts from their fragments. We built from scratch a synthetic dataset (DS) of fragments of different 3D objects including aging effects. We used this DS to train deep learning models for the task of classifying internal and external fragments. As model architectures, we adopted PointNet and dynamical graph convolutional neural network, which take as input a point cloud representing the spatial geometry of a fragment, and we optimized model performance by adding additional features sensitive to local geometry characteristics. We tested the approach by performing several experiments to check the robustness and generalization capabilities of the models. Finally, we test the models on a real case using a 3D scan of artifacts preserved in different museums, artificially fragmented, obtaining good performance.

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Section: Related Workmentioning

confidence: 99%

Section: Real Statues Models Dsmentioning

confidence: 99%

Artificial neural networks exploiting point cloud data for fragmented solid objects classification

Baiocchi,

Giagu,

Napoli

et al. 2023

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…E Altantsetseg et al [16] used a Fourier transform-based method to extract the curves on the fracture surfaces as the features to match the sections. E. Vendrell et al [17] provided a general solution to the problem of unconstrained reorganization, in which the feature points of the fragments were first extracted to generate the PFH descriptors that were matched, and then the geometric constraint was used to perform mismatch elimination.…”

Section: A Pairwise Matchingmentioning

confidence: 99%