\begin{table}[t]
\centering
\caption{Comparison of sparse-view reconstruction methods. We classify the priors used by representative methods into three categories：\textbf{Topology} (global geometry/structure), \textbf{Appearance} (photometric/frequency cues), and \textbf{Semantics} (high-level guidance). \Yes\ denotes explicit use, \No\ denotes not considered. TAS-GS is the only method that integrates all three priors while maintaining real-time performance.}
\label{tab:related_comparison}
\resizebox{\textwidth}{!}{
\begin{tabular}{lcccccc}
\toprule
Method & Views & Topology & Appearance & Semantics & Real-time & \makecell[c]{Remarks} \\
\midrule
NeRF~\citep{mildenhall2020nerf} & Dense & \No & \Yes & \No & \No &
\notesizegray{Implicit volumetric, high quality but slow} \\
Mip-NeRF~\citep{barron2021mipnerf} & Dense & \No & \Yes & \No & \No &
\notesizegray{Cone sampling, anti-aliasing, robustness} \\
IBRNet~\citep{wang2021ibrnet} & Sparse & \No & \Yes & \No & \No &
\notesizegray{Pixel-aligned interpolation for few views} \\
DS-NeRF~\citep{deng2021dsnerf} & Sparse & \Yes & \Yes & \No & \No &
\notesizegray{Depth supervision for sparse-view stability} \\
RegNeRF~\citep{niemeyer2022regnerf} & Sparse & \No & \Yes & \No & \No &
\notesizegray{Unseen-view consistency regularization} \\
GeoNeRF~\citep{johari2022geonerf} & Sparse & \Yes & \Yes & \No & \No &
\notesizegray{Epipolar geometric priors} \\
FreeNeRF~\citep{yang2023freenerf} & Sparse & \No & \Yes & \No & \No &
\notesizegray{Frequency curriculum to reduce overfitting} \\
SparseNeRF~\citep{wang2023sparsenerf} & Sparse & \No & \Yes & \No & \No &
\notesizegray{Ranking-based depth distillation} \\
3DGS~\citep{kerbl2023gaussiansplatting} & Dense & \No & \Yes & \No & \Yes &
\notesizegray{Explicit splats; real-time baseline} \\
DNGaussian~\citep{li2024dngaussian} & Sparse & \Yes & \Yes & \No & \Yes &
\notesizegray{Global--local depth normalization} \\
FSGS~\citep{zhu2024fsgs} & Sparse & \Yes & \Yes & \No & \Yes &
\notesizegray{Gaussian unpooling, virtual-view consistency} \\
CoR-GS~\citep{zhang2024corgs} & Sparse & \Yes & \Yes & \No & \Yes &
\notesizegray{Co-regularization to suppress floaters} \\
MVSplat~\citep{chen2024mvsplat} & Sparse & \No & \Yes & \No & \Yes &
\notesizegray{Efficient multi-view Gaussian initialization} \\
SCGaussian~\citep{cheng2024scgaussian} & Sparse & \Yes & \Yes & \No & \Yes &
\notesizegray{Matched-ray structure consistency} \\
FreeSplat~\citep{wang2024freesplat} & Sparse & \No & \Yes & \No & \Yes &
\notesizegray{Generalizable indoor free-view synthesis} \\
DropGaussian~\citep{park2025dropgaussian} & Sparse & \Yes & \Yes & \No & \Yes &
\notesizegray{Stochastic splat dropping for structure} \\
NexusGS~\citep{zheng2025nexusgs} & Sparse & \Yes & \Yes & \No & \Yes &
\notesizegray{Epipolar depth priors, robust fusion/pruning} \\
\midrule
\rowcolor{gray!15}
\textbf{TAS-GS (Ours)} & Sparse & \textbf{\Yes} & \textbf{\Yes} & \textbf{\Yes} & \Yes &
\notesizegray{Topology--Appearance--Semantics aware 3DGS} \\
\bottomrule
\end{tabular}}
\end{table}
