Authors :
Anay Dongre
Volume/Issue :
Volume 8 - 2023, Issue 1 - January
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3JQInR1
DOI :
https://doi.org/10.5281/zenodo.7597137
Abstract :
Neural Radiance Fields (NeRF) is a machine
learning model that can generate high-resolution,
photorealistic 3D models of scenes or objects from a set of
2D images. It does this by learning a continuous 3D function
that maps positions in 3D space to the radiance (intensity
and color) of the light that would be observed at that
position in the scene.
To create a NeRF model, the model is trained on a
dataset of 2D images of the scene or object, along with their
corresponding 3D positions and orientations. The model
learns to predict the radiance at each 3D position in the
scene by using a combination of convolutional neural
networks (CNNs) and a differentiable renderer.
Neural Radiance Fields (NeRF) is a machine
learning model that can generate high-resolution,
photorealistic 3D models of scenes or objects from a set of
2D images. It does this by learning a continuous 3D function
that maps positions in 3D space to the radiance (intensity
and color) of the light that would be observed at that
position in the scene.
To create a NeRF model, the model is trained on a
dataset of 2D images of the scene or object, along with their
corresponding 3D positions and orientations. The model
learns to predict the radiance at each 3D position in the
scene by using a combination of convolutional neural
networks (CNNs) and a differentiable renderer.
