We introduce LighthouseGS, a practical novel view synthesis framework based on 3D Gaussian Splatting that utilizes simple panorama-style captures from a single mobile device. While convenient, this rotation-dominant motion and narrow baseline make accurate camera pose and 3D point estimation challenging, especially in textureless indoor scenes. To address these challenges, LighthouseGS leverages rough geometric priors, such as mobile device camera poses and monocular depth estimation, and utilizes indoor planar structures. Specifically, we propose a new initialization method called plane scaffold assembly to generate consistent 3D points on these structures, followed by a stable pruning strategy to enhance geometry and optimization stability. Additionally, we present geometric and photometric corrections to resolve inconsistencies from motion drift and auto-exposure in mobile devices. Tested on real and synthetic indoor scenes, LighthouseGS delivers photorealistic rendering, outperforming state-of-the-art methods and enabling applications like panoramic view synthesis and object placement. Project page: https://vision3d-lab.github.io/lighthousegs/

翻译：我们提出了LighthouseGS，一种基于三维高斯溅射的实用新视角合成框架，该框架利用单移动设备采集的简易全景式图像序列。尽管采集方式便捷，但这种以旋转为主、基线狭窄的运动模式使得精确的相机位姿与三维点估计变得困难，尤其在纹理匮乏的室内场景中。为应对这些挑战，LighthouseGS利用粗略的几何先验（如移动设备相机位姿与单目深度估计），并充分结合室内平面结构。具体而言，我们提出了一种称为平面支架组装的新初始化方法，以在这些结构上生成一致的三维点，随后采用稳定的剪枝策略以提升几何重建与优化过程的稳定性。此外，我们提出了几何与光度校正方法，以解决移动设备中运动漂移与自动曝光导致的不一致问题。在真实与合成的室内场景测试中，LighthouseGS实现了照片级真实的渲染效果，其性能优于现有先进方法，并支持全景视图合成与物体摆放等应用。项目页面：https://vision3d-lab.github.io/lighthousegs/