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Neural radiance field - Wikipedia

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A neural radiance field (NeRF) is a method based on deep learning for reconstructing a three-dimensional representation of a scene from sparse two-dimensional images. The NeRF model can learn the scene geometry, camera poses, and the reflectance properties of objects, allowing it to render photorealistic views of the scene from novel viewpoints. First introduced in 2020[1], it has since gained significant attention for its potential applications in computer graphics and content creation.[2] The NeRF encodes the scene as a volumetric function optimized by a fully connected deep neural neural network (DNN), otherwise known as a Multilayer Perceptron (MLP). The MLP predicts a volume density and view-dependent emitted radiance given the spatial location (x, y, z) and viewing direction in Euler angles (θ, Φ) of the camera. By sampling many points along camera rays, traditional volume rendering techniques can produce an image. [1] A NeRF needs to be retrained for each unique scene. The first

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