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• W2912130451 abstract "Abstract. Fast but precise 3D reconstructions of cultural heritage scenes are becoming very requested in the archaeology and architecture. While modern multi-image 3D reconstruction approaches provide impressive results in terms of textured surface models, it is often the need to create a 3D model for which only a single photo (or few sparse) is available. This paper focuses on the single photo 3D reconstruction problem for lost cultural objects for which only a few images are remaining. We use image-to-voxel translation network (Z-GAN) as a starting point. Z-GAN network utilizes the skip connections in the generator network to transfer 2D features to a 3D voxel model effectively (Figure 1). Therefore, the network can generate voxel models of previously unseen objects using object silhouettes present on the input image and the knowledge obtained during a training stage. In order to train our Z-GAN network, we created a large dataset that includes aligned sets of images and corresponding voxel models of an ancient Greek temple. We evaluated the Z-GAN network for single photo reconstruction on complex structures like temples as well as on lost heritage still available in crowdsourced images. Comparison of the reconstruction results with state-of-the-art methods are also presented and commented." @default.
• W2912130451 created "2019-02-21" @default.
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• W2912130451 date "2019-01-31" @default.
• W2912130451 modified "2023-10-02" @default.
• W2912130451 title "GENERATIVE ADVERSARIAL NETWORKS FOR SINGLE PHOTO 3D RECONSTRUCTION" @default.
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• W2912130451 doi "https://doi.org/10.5194/isprs-archives-xlii-2-w9-403-2019" @default.
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