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Erschienen in:
Generative AI as a Supportive Tool for Scientific Research Kapitel lesen Erstes Kapitel lesen

2024 | OriginalPaper | Buchkapitel

Generating 3D Reconstructions Using Generative Models

verfasst von : Mehdi Malah, Ramzi Agaba, Fayçal Abbas

Erschienen in: Applications of Generative AI

Verlag: Springer International Publishing

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Abstract

As the capacity for visual representation continues to evolve, there is a growing need for techniques for realistic and efficient creation of three-dimensional objects. Generative models, particularly Generative Adversarial Networks, Variational Autoencoders and novel methods of Text-to-3D, utilize textual descriptions to generate 3D reconstructions with high-quality geometry and disentangled materials. In this chapter, we present an in-depth exploration of the application of generative models in 3D reconstruction. We begin by discussing the theoretical underpinnings of these models and their applicability to 3D reconstruction. This chapter studies how these models learn to generate new instances from a given distribution. We end with discussions of potential future directions and the broader impacts of these technologies in various industries.

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Vorheriges Kapitel Generative AI in Medical Imaging and Its Application in Low Dose Computed Tomography (CT) Image Denoising
Nächstes Kapitel ChatGPT Implementation in the Metaverse: Towards Another Level of Immersiveness in Education
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Metadaten
Titel
Generating 3D Reconstructions Using Generative Models
verfasst von
Mehdi Malah
Ramzi Agaba
Fayçal Abbas
Copyright-Jahr
2024
Buch
Applications of Generative AI

Print ISBN: 978-3-031-46237-5

Electronic ISBN: 978-3-031-46238-2

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-3-031-46238-2_20

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