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. Why Use Neural Fields? The Neural Fields model has a number of key features that make it particularly well-suited for generating high-quality 3D models from 2D images: Continuity: Because the NeRF model learns a continuous 3D function, it can generate smooth, continuous 3D models that do not have any "gaps" or