Abstract representations of 3D scenes are essential in computer vision, supporting tasks like mapping, localization, and surface reconstruction. Line segments are commonly used to capture scene structure, but existing 3D reconstruction methods often face limitations, either from instability in 2D projections or noise in direct 3D data. This paper introduces LineGS, a method that integrates geometry-guided 3D line reconstruction with a 3D Gaussian splatting model to improve accuracy. By leveraging Gaussian point densities along scene edges, LineGS refines initial line segments, aligning them more closely with the scene's geometric features. Experiments confirm that this approach enhances the fit to 3D structures, providing an efficient and reliable abstract representation of 3D scenes.
3D场景的抽象表示在计算机视觉中至关重要，支持诸如地图构建、定位和表面重建等任务。线段通常用于捕捉场景结构，但现有的3D重建方法常因2D投影的不稳定性或直接3D数据中的噪声而面临限制。 本文提出了一种名为LineGS的方法，将几何引导的3D线重建与3D高斯散射模型相结合，以提高精度。通过利用场景边缘附近的高斯点密度，LineGS能够优化初始线段，使其更贴合场景的几何特征。 实验结果表明，该方法能够增强3D结构的拟合效果，提供了一种高效且可靠的3D场景抽象表示方式。