Large-scale 3D reconstruction is critical in the field of robotics, and the potential of 3D Gaussian Splatting (3DGS) for achieving accurate object-level reconstruction has been demonstrated. However, ensuring geometric accuracy in outdoor and unbounded scenes remains a significant challenge. This study introduces LI-GS, a reconstruction system that incorporates LiDAR and Gaussian Splatting to enhance geometric accuracy in large-scale scenes. 2D Gaussain surfels are employed as the map representation to enhance surface alignment. Additionally, a novel modeling method is proposed to convert LiDAR point clouds to plane-constrained multimodal Gaussian Mixture Models (GMMs). The GMMs are utilized during both initialization and optimization stages to ensure sufficient and continuous supervision over the entire scene while mitigating the risk of over-fitting. Furthermore, GMMs are employed in mesh extraction to eliminate artifacts and improve the overall geometric quality. Experiments demonstrate that our method outperforms state-of-the-art methods in large-scale 3D reconstruction, achieving higher accuracy compared to both LiDAR-based methods and Gaussian-based methods with improvements of 52.6% and 68.7%, respectively.
大规模3D重建在机器人领域至关重要，3D Gaussian Splatting（3DGS）在实现精确的物体级别重建方面展示了其潜力。然而，在室外和无界场景中确保几何精度仍然是一个重要挑战。本研究引入了LI-GS，这是一种结合LiDAR和高斯散点的重建系统，用于提升大规模场景的几何精度。我们采用2D高斯表面单元作为地图表示，以增强表面对齐效果。此外，提出了一种新颖的建模方法，将LiDAR点云转换为平面约束的多模态高斯混合模型（GMM）。在初始化和优化阶段均使用GMM，以确保对整个场景的充分且连续的监督，同时减少过拟合的风险。GMM还用于网格提取，以消除伪影并提高整体几何质量。实验结果表明，我们的方法在大规模3D重建中优于现有最先进的方法，分别比基于LiDAR的方法和基于高斯的方法在精度上提升了52.6%和68.7%。