\section{Conclusion}

This paper presents \textbf{\Ours{}}, a topology–appearance–semantics-aware framework that extends 3D Gaussian Splatting for sparse-view novel view synthesis. In contrast to prior approaches, the proposed method effectively addresses key challenges including floaters, structural fragmentation, weak supervision, and underrepresented semantics. Extensive evaluations on LLFF and Mip-NeRF360 demonstrate consistent improvements over both NeRF- and Gaussian-based baselines in terms of distortion and perceptual metrics, while maintaining full compatibility with the standard 3DGS rasterizer. Ablation studies further validate the complementary benefits of each component, leading to cleaner textures, and more faithful reconstructions.

