Results 31 to 40 of about 61,584 (299)

radiance

open access: yesSpiritus: A Journal of Christian Spirituality, 2014
Citation: 'radiance' in the IUPAC Compendium of Chemical Terminology, 3rd ed.; International Union of Pure and Applied Chemistry; 2006. Online version 3.0.1, 2019. 10.1351/goldbook.R05037 • License: The IUPAC Gold Book is licensed under Creative Commons Attribution-ShareAlike CC BY-SA 4.0 International for individual terms.
Fabian Langguth, Michael Goesele
  +5 more sources

Fast Dynamic Radiance Fields with Time-Aware Neural Voxels [PDF]

open access: yesACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia, 2022
Neural radiance fields (NeRF) have shown great success in modeling 3D scenes and synthesizing novel-view images. However, most previous NeRF methods take much time to optimize one single scene. Explicit data structures, e.g.
Jiemin Fang   +7 more
semanticscholar   +1 more source

Temporal radiance caching [PDF]

open access: yesACM SIGGRAPH 2006 Sketches on - SIGGRAPH '06, 2006
We present a novel method for fast, high quality computation of glossy global illumination in animated environments. Building on the irradiance caching and radiance caching algorithms, our method leverages temporal coherence by sparse temporal sampling and interpolation of the indirect lighting. In our approach, part of the global illumination solution
Gautron, Pascal   +2 more
openaire   +6 more sources

NoPe-NeRF: Optimising Neural Radiance Field with No Pose Prior [PDF]

open access: yesComputer Vision and Pattern Recognition, 2022
Training a Neural Radiance Field (NeRF) without precomputed camera poses is challenging. Recent advances in this direction demonstrate the possibility of jointly optimising a NeRF and camera poses in forward-facing scenes.
Wenjing Bian   +4 more
semanticscholar   +1 more source

Solution of topical spectroradiometric problems using synchrotron radiation

open access: yesРоссийский технологический журнал, 2022
Objectives. In order to solve fundamental metrological problems concerning the reproduction and transmission of spectral radiometry units, as well as developing methods and tools for metrological support of modern technologies such as ...
A. S. Sigov   +5 more
doaj   +1 more source

NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections [PDF]

open access: yesComputer Vision and Pattern Recognition, 2020
We present a learning-based method for synthesizing novel views of complex scenes using only unstructured collections of in-the-wild photographs. We build on Neural Radiance Fields (NeRF), which uses the weights of a multi-layer perceptron to model the ...
Ricardo Martin-Brualla   +5 more
semanticscholar   +1 more source

BARF: Bundle-Adjusting Neural Radiance Fields [PDF]

open access: yesIEEE International Conference on Computer Vision, 2021
Neural Radiance Fields (NeRF) [31] have recently gained a surge of interest within the computer vision community for its power to synthesize photorealistic novel views of real-world scenes.
Chen-Hsuan Lin   +3 more
semanticscholar   +1 more source

Nerfies: Deformable Neural Radiance Fields [PDF]

open access: yesIEEE International Conference on Computer Vision, 2020
We present the first method capable of photorealistically reconstructing deformable scenes using photos/videos captured casually from mobile phones. Our approach augments neural radiance fields (NeRF) by optimizing an additional continuous volumetric ...
Keunhong Park   +6 more
semanticscholar   +1 more source

RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs [PDF]

open access: yesComputer Vision and Pattern Recognition, 2021
Neural Radiance Fields (NeRF) have emerged as a powerful representation for the task of novel view synthesis due to their simplicity and state-of-the-art performance.
M. Niemeyer   +5 more
semanticscholar   +1 more source

UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction [PDF]

open access: yesIEEE International Conference on Computer Vision, 2021
Neural implicit 3D representations have emerged as a powerful paradigm for reconstructing surfaces from multi-view images and synthesizing novel views.
Michael Oechsle   +2 more
semanticscholar   +1 more source

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