Generating new perspectives of an object from two-dimensional images using neural networks

Abstract

Provedeno je istraživanje Neural Radiance Fields metode i pixelNeRF okvira za učenje koji korištenjem neuronskih mreža provodi sintezu novih pogleda na predmet iz jedne ili nekoliko ulaznih slika. Korišten je PixelNeRF model treniran na ShapeNet skupu slika automobila prilagođen ulazu stvarnih fotografija automobila. Provedeno je testiranje modela s jednom do nekoliko ulaznih slika koristeći fotografije različitih automobila, kao i sintetički generiran skup slika. Kvaliteta novih pogleda na predmet očekivano raste s povećanjem broja ulaznih slika automobila, no već odlične rezultate prezentira i sinteza pogleda iz dvije te četiri ulazne fotografije.Research was conducted on the Neural Radiance Fields method and the pixelNeRF learning framework, which uses neural networks to synthesize new views of the subject using one or several input images. A PixelNeRF model trained on a ShapeNet set of car images and adapted to the input of real car photos was used. The model was tested with one to several input images using photos of different cars as well as a synthetically generated set of images. As expected, the quality of new views of the subject increases with the number of input car images, but excellent results are also presented by the synthesis of views from only two or four input photos

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Last time updated on 19/05/2024

This paper was published in FER Repository.

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