Gaussian Process Interpolation

Gaussian Process Interpolation is a statistical interpolation method that uses Gaussian processes to model and predict continuous elevation surfaces by estimating unknown values at spatial locations based on observed data points and their spatial covariance structure.

66

Publications

3.7K

Citations

205

Authors

124

Institutions

Publications per year

2017–2026

41

Authors

205

  1. AG
    Andrew Gordon Wilson

    Cornell University, United States

    3 publications · 266 citations

    3

    h-index

  2. HD
    Hugh Durrant‐Whyte

    The University of Sydney, Australia

    2 publications · 228 citations

    2

    h-index

  3. SV
    Shrihari Vasudevan

    Australian Centre for Robotic Vision, Australia

    2 publications · 228 citations

    2

    h-index

  4. BU
    Ben Upcroft

    Queensland University of Technology, Australia

    2 publications · 51 citations

    2

    h-index

  5. EN
    Eric Nettleton

    The University of Sydney, Australia

    2 publications · 228 citations

    2

    h-index

1–5 of 205

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 256 citations

    4

    h-index

  2. The Journal of Chemical Physics

    3 publications · 274 citations

    3

    h-index

  3. The Astrophysical Journal

    ApJ · 2 publications · 127 citations

    2

    h-index

  4. IEEE Transactions on Robotics

    1 publications · 32 citations

    2

    h-index

  5. Journal of Cosmology and Astroparticle Physics

    JCAP · 2 publications · 124 citations

    2

    h-index

1–5