User:Arobotwrotethis/sandbox/Neural radiance field
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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 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, it has since gained significant attention for its potential applications in computer graphics and content creation.
Algorithm
The NeRF encodes the scene as a volumetric function, which can be optimized from sparse input views (image and camera position). The optimizer is a fully connected deep neural neural network (DNN). The network input is the 5D camera coordinate including spatial location (x, y, z) and viewing direction in Euler angles (θ, Φ). The network's output is the volume density and view-dependent emitted radiance at that location. By sampling many points along camera rays, traditional volume rendering techniques can be used to produce an image. [1]
Training
For each sparse viewpoint provided, camera rays are marched through the scene, generating a set of 3D points with a given radiance direction (into the camera). These points are fed through the network to predict the volume density and emitted radiance. Then, an image is generated through classical volume rendering. Because this process is differentiable, the resulting predicted image and original image can be compared to produce a difference, called loss. This loss function is minimized with gradient descent over multiple viewpoints, encouraging the neural network to develop a coherent model of the scene.
Variations and Implementations
Early NeRFs were incredibly slow to optimize and required that all input views were taken with the same camera in the same lighting conditions. These performed best when limited to individual objects, such as a drum set, plants or Lego toys.[2]
-Positional Encoding
-Multiscale Representation
-Learned Initializations
In 2022, Nvidia pioneered a variant called Instant NeRFs that was much faster to optimize and render. Google researchers also reported new techniques for NeRF in the Wild, a system that is much more robust to different cameras, lighting conditions, and scene variations. This led to using NeRFs to generate content variations based on simulated lighting conditions. [2]
Applications
NeRFs, unlike photogrammetric methods, do not produce any 3D geometry. The scene is encoded in an underlying volumetric function. This limits their functionality to cases where the output image is used, rather than raw scene geometry. NeRFs have incredible potential in content creation, where on-demand photorealistic views are extremely valuable. [3] NeRFs can also be combined with classical rendering techniques to insert synthetic objects and create believable virtual experiences.
References
- ^ Mildenhall, Ben; Srinivasan, Pratul P.; Tancik, Matthew; Barron, Jonathan T.; Ramamoorthi, Ravi; Ng, Ren (2020). Vedaldi, Andrea; Bischof, Horst; Brox, Thomas; Frahm, Jan-Michael (eds.). "NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis". Computer Vision – ECCV 2020. Lecture Notes in Computer Science. Cham: Springer International Publishing: 405–421. doi:10.1007/978-3-030-58452-8_24. ISBN 978-3-030-58452-8.
- ^ a b "What is a Neural Radiance Field (NeRF)? | Definition from TechTarget". Enterprise AI. Retrieved 2023-10-24.
- ^ "Shutterstock Speaks About NeRFs At Ad Week | Neural Radiance Fields". neuralradiancefields.io. 2023-10-20. Retrieved 2023-10-24.
External links