3D reconstruction of incomplete archaeological objects using a generative adversarial network

Renato Hermoza, Ivan Sipiran

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

32 Citas (Scopus)


We 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.
Idioma originalEspañol
Título de la publicación alojadaACM International Conference Proceeding Series
Número de páginas7
EstadoPublicada - 11 jun. 2018
Publicado de forma externa

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