用视觉语言先验提升室内3D高斯点云的重建精度
PlanarGS: High-Fidelity Indoor 3D Gaussian Splatting Guided by Vision-Language Planar Priors
- 引入视觉语言引导的平面先验，增强低纹理区域几何约束
- 在ScanNet等数据集上显著优于现有方法，细节更丰富
- 适合需要高质量室内三维重建的研究与应用
三维高斯点云（3DGS）在新视角合成中表现优异，但在以大面积低纹理区域为主的室内场景中，依赖光度损失优化易导致几何模糊、难以恢复高保真表面。为此，本文提出PlanarGS，一种专为室内场景重建设计的3DGS框架。通过预训练的视觉-语言分割模型生成平面区域提议，并结合多视角融合与几何先验进行精修，构建语言提示的平面先验（LP3）。在3D高斯优化中引入两项新约束：平面一致性监督项和几何先验监督项，分别引导高斯分布保持平面特性并遵循深度与法向信息。在标准室内基准数据集上的大量实验表明，PlanarGS能重建精确且细节丰富的三维表面，性能显著超越现有最先进方法。项目主页：https://planargs.github.io
原文摘要 · Abstract (English)
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
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