PlanarGS: High‐Fidelity Indoor 3D Gaussian Splatting Guided by Vision–Language Planar Priors
Xirui Jin, Renbiao Jin, Boying Li, Danping Zou, Wenxian Yu
Three-dimensional Gaussian Splatting (3DGS) has recently emerged as an efficient representation for novel-view synthesis, achieving impressive visual quality. However, in scenes dominated by large and low-texture regions, common in indoor environments, the photometric loss used to optimize 3DGS yields ambiguous geometry and fails to recover high-fidelity 3D surfaces. To overcome this limitation, we introduce PlanarGS, a 3DGS-based framework tailored for indoor scene reconstruction. PlanarGS first employs a pretrained vision-language segmentation model to detect planar regions in images. It then refines these region proposals via cross-view fusion and integrates depth and surface-normal priors extracted from a pretrained 3D network. Finally, the 3D Gaussians are optimized with two additional terms: a planar-prior supervision term that enforces planar consistency, and a geometric-prior supervision term that steers the Gaussians toward the depth and normal cues. We have conducted extensive experiments on standard indoor benchmarks. The results show that PlanarGS reconstructs accurate and detailed 3D surfaces, consistently outperforming state-of-the-art methods by a large margin.