\section{Introduction}
\label{sec:introduction}

Novel view synthesis (NVS) \citep{avidan1997novel,watson2022novel} is a fundamental problem in computer vision with a wide range of applications, such as film and visual effects production, augmented reality, and robotics. High-quality NVS requires both accurate geometric reconstruction and faithful modeling of complex appearance, especially when extrapolating to unseen viewpoints.  

Neural Radiance Fields (NeRF)~\citep{mildenhall2020nerf} established the paradigm of volumetric neural rendering, achieving impressive fidelity but requiring dense input views and incurring heavy computational cost. Subsequent extensions improved quality and scalability by addressing anti-aliasing and unbounded scenes~\citep{barron2021mipnerf,barron2022mipnerf360,barron2023zipnerf}, accelerating convergence with explicit encodings~\citep{fridovichkeil2022plenoxels,mueller2022instantngp}, and enhancing rendering efficiency with voxel- or octree-based structures~\citep{yu2021plenoctrees,fridovichkeil2022plenoxels}. More recently, 3D Gaussian Splatting (3DGS)~\citep{kerbl2023gaussiansplatting} advanced explicit neural scene representations by replacing costly volumetric ray marching with tile-based differentiable splatting, achieving state-of-the-art fidelity–efficiency trade-offs and enabling real-time photorealistic novel view synthesis. 

Despite these advances, sparse-view reconstruction remains underexplored. With limited input supervision, optimization becomes ill-posed, often producing ambiguous geometry, floating artifacts, and blurred textures. NeRF-based methods address sparsity using priors such as depth supervision~\citep{deng2021dsnerf}, unseen-view consistency~\citep{niemeyer2022regnerf}, frequency regularization~\citep{yang2023freenerf}, or semantic guidance~\citep{jain2021dietnerf}, but they tend to converge slowly and yield unstable textures. Recent 3DGS variants mitigate floaters and structural collapse with structural priors and regularization. For example, FSGS~\citep{zhu2024fsgs} introduces unpooling and virtual training views, SCGaussian~\citep{cheng2024scgaussian} enforces multi-view consistency across matched rays, and NexusGS~\citep{zheng2025nexusgs} incorporates epipolar geometry constraints for robust pruning of misplaced Gaussians.
Other strategies include stochastic regularization~\citep{park2025dropgaussian}, cross-view co-regularization~\citep{zhang2024corgs}, and depth-aware normalization~\citep{li2024dngaussian}, while generalizable splatting models~\citep{wang2024freesplat,chen2024mvsplat} extend sparse-view training toward cross-scene generalization.
However, most existing approaches focus on a single type of prior. This limitation motivates our work: a unified framework that integrates topology, appearance, and semantic cues for robust sparse-view reconstruction. A qualitative comparison of representative methods is given in Table~\ref{tab:related_comparison}. 

We present TAS-GS, a topology–appearance–semantics aware framework for sparse-view 3DGS. Our design explicitly unifies three complementary forms of guidance.
First, a topology-aware graph regularizer prunes floaters and repairs geometric voids, ensuring structural integrity before appearance learning.
Second, a GNN-based appearance propagation module propagates color and opacity across geometry-aware neighborhoods with visibility-adaptive residual blending, improving photometric coherence under weak supervision.
Third, a semantic-rarity and boundary-aware
modulator emphasizes rare categories and occlusion edges, sharpening contours and preserving underrepresented structures.
Our approach applies all modules exclusively during training, producing Gaussians that are fully compatible with standard 3DGS rasterization. In contrast, methods that rely on inference-time structures ~\citep{su2024surfgaussian,feng2023neusplat} alter the Gaussian representation, thus limiting compatibility and increasing runtime complexity. Extensive experiments on LLFF~\citep{llff} and Mip-NeRF 360~\citep{barron2022mipnerf360} demonstrate that \Ours{} consistently outperforms NeRF- and Gaussian-based baselines, both quantitatively and qualitatively.

Our contributions are summarized as follows:
\begin{itemize}[leftmargin=*]
    \item We introduce TAS-GS, the first framework to jointly integrate topology-, appearance-, and semantics-aware priors for sparse-view 3D Gaussian Splatting.
    \item We design a topology-aware graph regularizer that enforces connectivity and removes floaters through graph-based pruning and hole repair.
    \item We propose a GNN-based appearance propagation module with visibility-adaptive residual blending to propagate reliable attributes into weakly supervised regions.
    \item We develop a semantic-rarity and boundary-aware modulator to focus supervision on rare classes and edge pixels, preserving fine structures and preventing texture bleeding.
\end{itemize}

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\input{tables/main/comparisonrelated}