Use of Neural Radiance Field (NeRF) Models for  Extracting 3D Features of Objects
Giorgio De Magistris, Juan David Rodriguez and Christian Napoli
Journal of Data Processing, Vol. 14, No. 2, 2024.
DOI: https://doi.org/10.6025/jdp/2024/14/2/67-78
Link: https://www.dline.info/jdp/fulltext/v14n2/jdpv14n2_3.pdf
Abstract:
Our research takes a bold step in the field of rendering, focusing on an innovative approach that seamlessly blends 3D objects into reconstructed 3D spaces, offering fresh perspectives of the scene. By using Neural Radiance Field (NeRF) models to create detailed 3D spaces and depth estimation algorithms to extract 3D features of objects from single images, we aim to align the depth information from the NeRF model with the depth data of the added object. This precise and spatially consistent placement of the object in the scene not only enhances the realism of the virtual world but also opens up new possibilities in the field of computer graphics.