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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 enables downstream applications of novel view synthesis, scene geometry reconstruction, and obtaining the reflectance properties of the scene.
Neural modeling field (NMF) is a mathematical framework for machine learning which combines ideas from neural networks, fuzzy logic, and model based recognition. It has also been referred to as modeling fields , modeling fields theory (MFT), Maximum likelihood artificial neural networks (MLANS).
The term light field was coined by Andrey Gershun in a classic 1936 paper on the radiometric properties of light in three-dimensional space. The term "radiance field" may also be used to refer to similar, or identical [2] concepts. The term is used in modern research such as neural radiance fields
Gaussian splatting model of a collapsed building taken from drone footage. 3D Gaussian splatting is a technique used in the field of real-time radiance field rendering. [3] It enables the creation of high-quality real-time novel-view scenes by combining multiple photos or videos, addressing a significant challenge in the field.
Dataset name Brief description Preprocessing Instances Format Default task Created (updated) Reference Creator Aff-Wild 298 videos of 200 individuals, ~1,250,000 manually annotated images: annotated in terms of dimensional affect (valence-arousal); in-the-wild setting; color database; various resolutions (average = 640x360)
IBM Granite is a series of decoder-only AI foundation models created by IBM. [3] It was announced on September 7, 2023, [4] [5] and an initial paper was published 4 days later. [6]
If it seems like you and everyone around you are getting sick this winter, you're not wrong. Experts say this is the worst flu season in the U.S. in more than a decade and cases are still trending ...
In mathematics, a Relevance Vector Machine (RVM) is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression and probabilistic classification. [1]