\section{Conclusion, Limitations and Future Directions}

This paper presents a novel NeRF framework under limited samples using Mutual Information Theory. We introduce mutual information from both macro (semantic space) and micro (pixel space) perspectives in different settings. In sparse view sampling, we employ a greedy algorithm to minimize mutual information. In few-shot view synthesis, we utilize plug-and-play regularization terms to maximize it. Experiments across different settings validate the robustness of our framework.

Our framework has some limitations, particularly in terms of comparisons with the diffusion-based methods. We were unable to include these comparisons due to the lack of open-source code or differing dataset settings, which are detailed in the appendix. Future work should aim to incorporate more methods and explore additional variations within our framework.
