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dc.contributor.advisorSipiran Mendoza, Iván Anselmo
dc.contributor.authorHermoza Aragonés, Renato
dc.description.abstractWe introduce a data-driven approach to aid the repairing and conservation of archaeological objects: ORGAN, an object reconstruction generative adversarial network (GAN). By using an encoder-decoder 3D deep neural network on a GAN architecture, and combining two loss objectives: a completion loss and an Improved Wasserstein GAN loss, we can train a network to effectively predict the missing geometry of damaged objects. As archaeological objects can greatly differ between them, the network is conditioned on a variable, which can be a culture, a region or any metadata of the object. In our results, we show that our method can recover most of the information from damaged objects, even in cases where more than half of the voxels are missing, without producing many errors.
dc.publisherPontificia Universidad Católica del Perú
dc.sourcePontificia Universidad Católica del Perú
dc.sourceRepositorio de Tesis - PUCP
dc.subjectRedes neuronales (Computación)
dc.subjectInteligencia artificial--Aplicaciones
dc.title3D Reconstruction of Incomplete Archaeological Objects Using a Generative Adversarial Network
dc.typeinfo:eu-repo/semantics/masterThesisíster en Informática con mención en Ciencias de la Computaciónes_ESíaes_ES Universidad Católica del Perú. Escuela de Posgradoes_ESática con mención en Ciencias de la Computaciónes_ES
dc.type.otherTesis de maestría

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