PlanarGS：由视觉语言平面先验引导的高保真室内三维高斯泼溅
PlanarGS: High-Fidelity Indoor 3D Gaussian Splatting Guided by Vision-Language Planar Priors
AI总结：
针对室内低纹理场景下3DGS重建几何模糊的问题，提出融合视觉语言平面先验的PlanarGS框架，通过平面与几何监督项优化高斯分布，在室内基准上大幅超越现有方法。
摘要：
三维高斯泼溅（3DGS）近期作为一种高效的新视角合成表征方法兴起，取得了令人瞩目的视觉质量。然而，在室内环境中常见的以大面积低纹理区域为主的场景里，用于优化3DGS的光度损失会产生模糊的几何结构，无法恢复高保真三维表面。为克服这一局限，我们提出了PlanarGS，这是一款专为室内场景重建定制的基于3DGS的框架。具体而言，我们设计了语言提示平面先验（Language-Prompted Planar Priors, LP3）流水线，该流水线采用预训练视觉语言分割模型，并通过跨视图融合与几何先验检验来优化其区域提议。我们框架中的三维高斯通过两个额外项进行优化：一项是强制平面一致性的平面先验监督项，另一项是引导高斯趋近深度与法向线索的几何先验监督项。我们在标准室内基准上开展了大量实验，结果表明PlanarGS能够重建出精准且细节丰富的三维表面，始终以大幅优势超越当前最优方法。项目页面：https://planargs.github.io
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
