Machine learningDeep Learning, 3D Vision, Generative Models

Neural Radiance Fields (NeRF)

Neural Radiance Fields (NeRF) is a method introduced by Mildenhall et al. in 2020 that represents a 3D scene as a continuous function parameterized by a neural network. Given multi-view images of a scene, NeRF learns to predict the color and density of light rays at any spatial location and viewing angle, enabling novel view synthesis with photorealistic quality.

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Sources

  1. Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2020). NeRF: Representing scenes as neural radiance fields for view synthesis. In Computer Vision-ECCV 2020: 16th European Conference (pp. 405-421). Springer International Publishing. DOI: 10.1007/978-3-030-58452-8_24

Related methods

Referenced by

ScholarGateNeural Radiance Fields (NeRF) (NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis). Retrieved 2026-06-04 from https://scholargate.app/tr/deep-learning/neural-radiance-fields