\begin{table*}[t!]
\centering

\begin{tabular}{c|cc|cc|ccc}
\hline
% \textbf{Method} & \textbf{PSNR} $\uparrow$ & \textbf{SSIM} $\uparrow$ & \textbf{LPIPS} $\downarrow$  & \textbf{PSNR} $\uparrow$ & \textbf{SSIM} $\uparrow$ & \textbf{LPIPS} $\downarrow$ \\

\multirow{2}{*}{\textbf{Method}} & \multicolumn{2}{c|}{\textbf{DTU}(Object)} & \multicolumn{2}{c|}{\textbf{DTU}(Full image)} & \multicolumn{3}{c}{\textbf{LLFF}} \\
% \cline{2-7}
 & \textbf{PSNR} $\uparrow$ & \textbf{SSIM} $\uparrow$ & \textbf{PSNR} $\uparrow$ & \textbf{SSIM} $\uparrow$  & \textbf{PSNR} $\uparrow$ & \textbf{SSIM} $\uparrow$ & \textbf{LPIPS} $\downarrow$ \\
\hline

% DietNeRF~\cite{jain2021putting}  &  22.503   & 0.823   & 0.124  \\

\hline
\hline

Mip-NeRF
& 9.10
& 0.578
& 7.94
& 0.235
& 16.11     
& 0.401  
& 0.460\\

DiffusioNeRF
&  16.20
&  0.698
&  /
& /
& 19.79 & 0.568 &   0.338 \\

DietNeRF
& 11.85
& 0.633
& 10.01
& 0.354
& 14.94     
&0.370   
&0.496 \\

\textbf{DietNeRF+Ours}
& 13.04
& 0.711
& 11.95
& 0.410
& 16.01     
&0.433   
&0.443  \\

% RegNeRF 
% & 19.08      
% &0.668   
% &0.283 \\
% \hline
% \hline

RegNeRF
& 18.50
& 0.744
& 15.00
& 0.606
& 18.84    
& 0.573 
&0.345 \\

\textbf{RegNeRF+Ours}
& 19.78
& 0.791
& 15.79
& 0.634
& 19.44     
&0.611   
&0.322  \\

FreeNeRF
&  19.92
&  0.787
&  18.02
& 0.680
& 19.63 & 0.612 &   0.308 \\


\hline
\hline
% \textbf{FreeNeRF+Ours}


 \multirow{2}{*}{\textbf{FreeNeRF+Ours}}
& \textbf{20.42}
& \textbf{0.814}
&\textbf{ 18.63}
& \textbf{0.712}
& \textbf{20.17 } & \textbf{ 0.634 } & \textbf{ 0.274}  \\

& \textbf{(+0.50)}
& \textbf{(+0.027)}
& \textbf{(+0.61)}
&\textbf{ (+0.032)}
& \textbf{(+0.54)}
& \textbf{(+0.022)}
& \textbf{(-0.034)} \\
\bottomrule
\end{tabular}
\vspace{-1.5mm}
\caption{\textbf{Quantitative comparison on LLFF and DTU.} There are 3 input views  for training, consistent with FreeNeRF. On DTU, we use objects’ masks to remove the background when computing metrics, as full-image evaluation is biased
towards the background, as reported by~\citep{yu2021pixelnerf,niemeyer2022regnerf}.} 
\vspace{-1.5mm}
\label{tab:blender}
\end{table*}

% \begin{tabular}{c|c|c|c}
% \hline
% \textbf{Method} & \textbf{PSNR} $\uparrow$ & \textbf{SSIM} $\uparrow$ & \textbf{LPIPS} $\downarrow$  \\
% \hline
% \hline
% NeRF~\cite{mildenhall2020nerf}     & 14.934 & 0.687  & 0.318 \\
% NV~\cite{lombardi2019neural} &  17.859   & 0.741 & 0.245  \\
% \quad\quad Simplified NeRF~\cite{jain2021putting} \quad\quad  & 20.092 & 0.822 & 0.179  \\
% % DietNeRF \cite{jain2021putting} & 23.147     &  0.866   &  0.109  \\
% % DietNeRF + $\mathcal{L}_\text{MSE}$ ft $50k$  &  23.591 &  0.874 &  0.097 \\ 
% % \hline
% % \hline
% % NeRF (repro.) &  13.931 & 0.689 &  0.320 \\
% NeRF + $L_{micro}$  &  20.101   & 0.799   & 0.151  \\
% NeRF + $L_{macro}$ (DietNeRF~\cite{jain2021putting})  &  22.503   & 0.823   & 0.124  \\
% % DietNeRF~\cite{jain2021putting}  &  22.503   & 0.823   & 0.124  \\
% \textbf{NeRF + $L_{micro}$ + $L_{macro}$ (Ours)} &  22.860~(+0.360)  & 0.859~(+0.036) &  0.114~(-0.01)  \\
% \hline
% \hline
% FreeNeRF~\cite{yang2023freenerf} &  24.259 & 0.883 &   0.098 \\
% \textbf{FreeNeRF+Ours} &  \textbf{ 24.896~(+0.637) } & \textbf{ 0.894~(+0.011) } & \textbf{ 0.096~(-0.002)}  \\
% \bottomrule
% \end{tabular}
% % \vspace{-2mm}
% \caption{\textbf{Quantitative comparison on Blender.} There are 8 input views  for training, consistent with FreeNeRF.} 
% % \vspace{-1mm}
% \label{tab:blender}
% \end{table*}

\begin{table*}[t!]
\centering

\begin{tabular}{c|c|c|c}
\hline
\textbf{Method} & \textbf{PSNR} $\uparrow$ & \textbf{SSIM} $\uparrow$ & \textbf{LPIPS} $\downarrow$  \\
\hline
\hline
NeRF     & 14.934 & 0.687  & 0.318 \\
NV &  17.859   & 0.741 & 0.245  \\
\quad\quad Simplified NeRF \quad\quad  & 20.092 & 0.822 & 0.179  \\
% DietNeRF \cite{jain2021putting} & 23.147     &  0.866   &  0.109  \\
% DietNeRF + $\mathcal{L}_\text{MSE}$ ft $50k$  &  23.591 &  0.874 &  0.097 \\ 
% \hline
% \hline
% NeRF (repro.) &  13.931 & 0.689 &  0.320 \\
NeRF + $L_{micro}$  &  20.101~(+5.167)   & 0.799(+0.112)   & 0.151(-0.167)  \\
NeRF + $L_{macro}$ (DietNeRF)  &  22.503~(+7.569)   & 0.823~(+0.136)   & 0.124(-0.194)  \\
% DietNeRF~\cite{jain2021putting}  &  22.503   & 0.823   & 0.124  \\
\textbf{ \ NeRF + $L_{micro}$ + $L_{macro}$ (Ours) \ } &  \textbf{23.394~(+8.460)}  & \textbf{0.859~(+0.172)} &  \textbf{0.103~(-0.215)}  \\
\hline
\hline
FreeNeRF &  24.259 & 0.883 &   0.098 \\
\textbf{FreeNeRF+Ours} &  \textbf{ \ 24.896~(+0.637) \ } & \textbf{ \  0.904~(+0.021) \ } & \textbf{ \ 0.086~(-0.012) \ }  \\
\bottomrule
\end{tabular}
\vspace{-1.5mm}
\caption{\textbf{Quantitative comparison on Blender.} There are 8 input views  for training, consistent with FreeNeRF. For DietNeRF, the consistency loss actually belongs to the $L_{\text{macro}}$, so DietNeRF is a degradation of our framework.} 
\vspace{-1.7mm}
\label{tab:ablation}
\end{table*}