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\title{MutualNeRF: Improve the Performance of NeRF under Limited Samples with Mutual Information Theory}

% The standard author block has changed for UAI 2025 to provide
% more space for long author lists and allow for complex affiliations
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% All author information is authomatically removed by the class for the
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\usepackage{footmisc}
\author[* 1 2]{Zifan Wang}
\author[* 1 2]{Jingwei Li}
\author[1]{Yitang Li}
\author[$\dagger$ 1 2]{Yunze Liu}

% Add affiliations after the authors
\affil[1]{%
    Institute for Interdisciplinary Information Sciences \\
    Tsinghua University
}
\affil[2]{%
    Shanghai Qizhi Institute
}
\affil[*]{Equal contribution}
\affil[$\dagger$]{Corresponding author}

  \begin{document}
\maketitle

\begin{abstract}
  This paper introduces MutualNeRF, a framework enhancing Neural Radiance Field (NeRF) performance under limited samples using Mutual Information Theory. While NeRF excels in 3D scene synthesis, challenges arise with limited data and existing methods that aim to introduce prior knowledge lack theoretical support in a unified framework. We introduce a simple but theoretically robust concept, Mutual Information, as a metric to uniformly measure the correlation between images, considering both macro (semantic) and micro (pixel) levels.
  For sparse view sampling, we strategically select additional viewpoints containing more non-overlapping scene information by minimizing mutual information without knowing ground truth images beforehand. Our framework employs a greedy algorithm, offering a near-optimal solution.
  For few-shot view synthesis, we maximize the mutual information between inferred images and ground truth, expecting inferred images to gain more relevant information from known images. This is achieved by incorporating efficient, plug-and-play regularization terms.
  Experiments under limited samples show consistent improvement over state-of-the-art baselines in different settings, affirming the efficacy of our framework.
\end{abstract}

\input{text/intro}
\input{text/related}
\input{text/method}
\input{text/exp}
\input{text/conclusion}

% References
\bibliography{uai2025-template}

\newpage

\onecolumn

\title{MutualNeRF: Improve the Performance of NeRF under Limited Samples with Mutual Information Theory \\(Supplementary Material)}
\maketitle

\appendix
\input{text/proof}

\input{text/supp}
\end{document}
