Neural Radiance Fields for Relighting and View Synthesis

De Matteo, Riccardo (2022) Neural Radiance Fields for Relighting and View Synthesis. [Laurea magistrale], Università di Bologna, Corso di Studio in Artificial intelligence [LM-DM270], Documento full-text non disponibile
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Neural scene representation and neural rendering are new computer vision techniques that enable the reconstruction and implicit representation of real 3D scenes from a set of 2D captured images, by fitting a deep neural network. The trained network can then be used to render novel views of the scene. A recent work in this field, Neural Radiance Fields (NeRF), presented a state-of-the-art approach, which uses a simple Multilayer Perceptron (MLP) to generate photo-realistic RGB images of a scene from arbitrary viewpoints. However, NeRF does not model any light interaction with the fitted scene; therefore, despite producing compelling results for the view synthesis task, it does not provide a solution for relighting. In this work, we propose a new architecture to enable relighting capabilities in NeRF-based representations and we introduce a new real-world dataset to train and evaluate such a model. Our method demonstrates the ability to perform realistic rendering of novel views under arbitrary lighting conditions.

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
De Matteo, Riccardo
Relatore della tesi
Correlatore della tesi
Corso di studio
Ordinamento Cds
Parole chiave
NeRF,Neural Rendering,Computer Vision,Computer Graphics,Machine Learning,Deep Learning,Neural Network,Artificial Intelligence,3D Reconstruction
Data di discussione della Tesi
6 Dicembre 2022

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