Neural Radiance Fields

Neural Radiance Fields is a computational framework that represents a 3D scene as a continuous volumetric radiance field, parameterized by a neural network to model the color and density of points in space, enabling synthesis of photorealistic novel views from sparse 2D image inputs.

524

Publications

49.5K

Citations

1.5K

Authors

271

Institutions

Publications per year

2017–2026

523

Authors

1.5K

  1. JT
    Jonathan T. Barron

    Google (United States), United States

    35 publications · 14.1K citations

    28

    h-index

  2. BM
    Ben Mildenhall

    Google (United States), United States

    24 publications · 12.3K citations

    19

    h-index

  3. PP
    Pratul P. Srinivasan

    Google (United States), United States

    19 publications · 11.1K citations

    16

    h-index

  4. PH
    Peter Hedman

    Google (United States), United States

    17 publications · 4.3K citations

    15

    h-index

  5. MT
    Matthew Tancik

    University of California, Berkeley, United States

    15 publications · 11.4K citations

    14

    h-index

1–5 of 1.5K

Venues

  1. 43

    h-index

  2. arXiv (Cornell University)

    Repository · 93 publications · 4.8K citations

    35

    h-index

  3. ACM Transactions on Graphics

    TOG · 22 publications · 2.4K citations

    20

    h-index

  4. 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

    Conference · 20 publications · 4.3K citations

    19

    h-index

  5. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 8 publications · 785 citations

    10

    h-index

1–5