PlanarGS: High-Fidelity Indoor 3D Gaussian Splatting Guided by Vision-Language Planar Priors

TL;DR

PlanarGS introduces a novel indoor 3D scene reconstruction method that leverages vision-language planar priors and geometric cues to improve the fidelity of Gaussian Splatting, outperforming existing techniques.

Contribution

It proposes a new framework combining vision-language priors with geometric supervision to enhance indoor 3D Gaussian Splatting reconstruction.

Findings

Outperforms state-of-the-art methods on indoor benchmarks

Produces more accurate and detailed 3D surfaces

Effectively integrates vision-language priors with geometric cues

Abstract

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. Specifically, we design a pipeline for Language-Prompted Planar Priors (LP3) that employs a pretrained vision-language segmentation model and refines its region proposals via cross-view fusion and inspection with geometric priors. 3D Gaussians in our framework 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. Project page: https://planargs.github.io