% \begin{table*}[!ht]
%     \centering
%     \caption{Quantitative Comparison of Registration performance with GT. \textbf{Bold} and \underline{underline} highlights the best and second best performance among algorithm registration results. * highlight significant improvements (paires t-test, p\textless 0.05). The proposed LOTUS-based methods significantly decrease the rotation and translation error compared to the baselines.\xxx{again way too many digits. for mse 0.014 +- 0.014. 23.2+-9.4 for psnr. 43.4+-34.8 for er. 25.6+-23.5 for et.} \xxx{add  p-values (*, **, etc) from t-test if you have time.} \xxx{the underline is very hard to see because the rows are so thin. i'm not sure why (or how) the font is smaller here than table 2?} \xxx{also: what do you want them to take away from this table? put it in the caption}}
%     \begin{adjustbox}{width=\textwidth}
%     \setlength{\tabcolsep}{1pt}
%     \renewcommand{\arraystretch}{1.3}
%     \begin{tabular}{|l|cccc|cc|}
%     \hline
%         \multirow{2}{*}{Method} & \multicolumn{4}{c|}{Similarity Metrics} & \multicolumn{2}{c|}{Transformation Error}\\ 
%         \cline{2-7}
%         ~ & NCC ↑ & SSIM ↑ & MSE($\times 100$) ↓ & PSNR ↑ & $\lvert ER\rvert$ ↓ & $\Vert ET\Vert$ ↓ \\ \hline
%         Greedy & 0.84$\pm$0.22 & 0.61$\pm$0.25 & 1.44$\pm$1.42 & 23.23$\pm$9.42 & 43.41$\pm$34.82 & 25.62$\pm$23.53  \\ \hline
%         ANTs & 0.85$\pm$0.11 & 0.57$\pm$0.23 & 1.49$\pm$0.95 & 20.90$\pm$6.68 & 55.09$\pm$33.79 & 30.288$\pm$20.68  \\ \hline
%         LOTUS(Greedy) & \underline{0.89$\pm$0.30} & \underline{0.75$\pm$0.17} & \underline{0.93$\pm$1.86} & \underline{23.79$\pm$4.29} & \underline{17.34$\pm$26.79*} & \underline{11.28$\pm$18.99*} \\ \hline
%         LOTUS(ANTs)) & \textbf{0.99$\pm$0.02*} & \textbf{0.85$\pm$0.07*} & \textbf{0.256$\pm$0.21*} & \textbf{26.58$\pm$2.01*} & \textbf{8.04$\pm$17.67*} & \textbf{4.33$\pm$10.05*}  \\ \hline
        
%         % MB-Single Reproduced & 0.135 & 0.248 & 85.6\% & 89.9\% & 89.4\% & 89.6\% & 0.792 & 0.447 \\ \hline
%         % \multicolumn{4}{l}{$^{\mathrm{a}}$Single means single-subject model.}
%     \end{tabular}
%     \end{adjustbox}
%     \label{tab:reg_res_whole_gtfinal}
% \end{table*}

\begin{table*}[t]
    \centering
    \caption{Registration performance on typical cases, using the GT for evaluation. \textbf{Best} and \underline{second}  performances are highlighted. LOTUS outpainting significantly improves performance (*, paired t-tests, p\textless 0.05) compared to the baselines.}
    \begin{adjustbox}{width=\textwidth}
    \setlength{\tabcolsep}{5pt}
    \renewcommand{\arraystretch}{0.9}
    \begin{tabular}{l|cccc|cc}
    \hline
        \multirow{2}{*}{Method} & \multicolumn{4}{c|}{Similarity Metrics} & \multicolumn{2}{c}{Transformation Error}\\ 
        \cline{2-7}
        ~ & NCC ↑ & SSIM ↑ & MSE($\times 100$) ↓ & PSNR ↑ & ER ↓ & ET ↓ \\ \hline
        Greedy & 0.839$\pm$0.219 & 0.612$\pm$0.252 & 1.44$\pm$1.42 & 23.2$\pm$9.4 & 43.4$\pm$34.8 & 25.6$\pm$23.5  \\ 
        % \hline
        ANTs & 0.851$\pm$0.113 & 0.565$\pm$0.226 & 1.49$\pm$0.95 & 20.9$\pm$6.7 & 55.1$\pm$33.8 & 30.3$\pm$20.7  \\ 
         \hline
        LOTUS(Greedy) & \underline{0.887$\pm$0.298} & \underline{0.746$\pm$0.169} & \underline{0.928$\pm$1.858} & \underline{23.8$\pm$4.3} & \underline{17.3$\pm$26.8*} & \underline{11.3$\pm$19.0*} \\ 
        % \hline
        LOTUS(ANTs)) & \textbf{0.985$\pm$0.023*} & \textbf{0.848$\pm$0.070*} & \textbf{0.255$\pm$0.210*} & \textbf{26.6$\pm$2.0*} & \textbf{8.04$\pm$17.67*} & \textbf{4.33$\pm$10.05*}  \\ \hline
        
        % MB-Single Reproduced & 0.135 & 0.248 & 85.6\% & 89.9\% & 89.4\% & 89.6\% & 0.792 & 0.447 \\ \hline
        % \multicolumn{4}{l}{$^{\mathrm{a}}$Single means single-subject model.}
    \end{tabular}
    \end{adjustbox}
    \label{tab:appendix:reg_res_whole_gtfinal}
\end{table*}