Neural Radiance Fields

Neural Radiance Fields

A neural radiance field (NeRF) is a fully-connected neural network that can generate novel views of complex 3D scenes, based on a partial set of 2D images.

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Neural radiance fields (NRFs) are a type of machine learning model that can be used for image generation and image-to-image translation tasks. They were introduced in the paper "Neural Radiance Fields for Image-Based Lighting" by Karsch et al. in 2014.

NRFs are based on the idea of representing an image as a probability distribution over the space of possible images, rather than as a fixed set of pixel values. This allows NRFs to capture complex, high-dimensional distributions and to generate diverse, realistic samples from the distribution.

NRFs are implemented using a neural network, which is trained to predict the radiance field (a measure of the amount of light emitted by a surface) for an input image. The network takes an input image and produces an output radiance field, which can then be used to synthesize a new image by sampling from the distribution. NRFs can also be used for image-to-image translation tasks, such as turning a day image into a night image or converting a photograph of a person into a cartoon.

Overall, NRFs are a powerful tool for image generation and image-to-image translation tasks and have been successful in producing high-quality, realistic images.