用3D高斯点优化线段，提升场景结构重建精度
LineGS : 3D Line Segment Representation on 3D Gaussian Splatting
- 将3D高斯点与几何线段结合，通过点云分布精修初始线段
- 在真实场景数据上实现更精确的线段定位与更紧凑的模型表示
- 适合需要高精度结构建模的三维重建与SLAM应用
3D场景的抽象表示在计算机视觉中至关重要，支持地图构建、定位、表面重建及SLAM、渲染等任务。线段因其能简洁捕捉场景结构特征而被广泛使用。然而，现有3D重建方法面临挑战：依赖2D投影的方法易受多视角匹配误差和遮挡影响，直接3D方法则受限于点云数据的噪声与稀疏性。本文提出LineGS，将几何引导的3D线段重建与3D高斯点阵模型结合，利用场景边缘处高密度的高斯点分布，优化传统几何方法生成的初始线段。通过与场景底层几何特征对齐，实现更精准可靠的3D结构表达。实验表明，相比基线方法，该方法在几何精度和模型紧凑性方面均有显著提升。
原文摘要 · Abstract (English)
Abstract representations of 3D scenes play a crucial role in computer vision, enabling a wide range of applications such as mapping, localization, surface reconstruction, and even advanced tasks like SLAM and rendering. Among these representations, line segments are widely used because of their ability to succinctly capture the structural features of a scene. However, existing 3D reconstruction methods often face significant challenges. Methods relying on 2D projections suffer from instability caused by errors in multi-view matching and occlusions, while direct 3D approaches are hampered by noise and sparsity in 3D point cloud data. This paper introduces LineGS, a novel method that combines geometry-guided 3D line reconstruction with a 3D Gaussian splatting model to address these challenges and improve representation ability. The method leverages the high-density Gaussian point distributions along the edge of the scene to refine and optimize initial line segments generated from traditional geometric approaches. By aligning these segments with the underlying geometric features of the scene, LineGS achieves a more precise and reliable representation of 3D structures. The results show significant improvements in both geometric accuracy and model compactness compared to baseline methods.
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