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
66
StrongSORT: Make DeepSORT Great Again
Yunhao Du, Zhicheng Zhao, Yang Song et al. · IEEE Transactions on Multimedia · 2023 · 1K citations
Gaussian process modeling of large‐scale terrain
Shrihari Vasudevan, Fábio Ramos, Eric Nettleton et al. · Journal of Field Robotics · 2009 · 170 citations
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124
University College London
London, United Kingdom
6 h-index
13 publications
589 citations
University of Sheffield
Sheffield, United Kingdom
4 h-index
14 publications
600 citations

The University of Sydney
Sydney, Australia
4 h-index
13 publications
1K citations
Tsinghua University
Beijing, China
4 h-index
4 publications
96 citations

University of Bristol
Bristol, United Kingdom
3 h-index
5 publications
213 citations
University of Oxford
Oxford, United Kingdom
3 h-index
3 publications
59 citations
Australian Centre for Robotic Vision
Brisbane, Australia
3 h-index
11 publications
969 citations
Max Planck Institute for Radio Astronomy
Bonn, Germany
2 h-index
2 publications
45 citations
1–8 of 124
Repository · 3 publications · 256 citations
4
h-index
3 publications · 274 citations
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ApJ · 2 publications · 127 citations
2
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1 publications · 32 citations
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JCAP · 2 publications · 124 citations
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