融合激光雷达与高斯点云，实现大场景高精度三维重建。
LI-GS: Gaussian Splatting with LiDAR Incorporated for Accurate Large-Scale Reconstruction
- 用2D高斯面元和多模态高斯混合模型提升表面对齐与几何精度。
- 在真实大场景中相较同类方法精度提升52.6%（比激光雷达）和68.7%（比高斯方法）。
- 适合需要高保真室外大场景重建的机器人与自动驾驶应用。
大规模3D重建在机器人领域至关重要，3D高斯点云（3DGS）已展现出物体级重建的潜力，但在户外无界场景中保持几何精度仍是重大挑战。本文提出LI-GS，一种融合激光雷达与高斯点云的重建系统，以提升大场景几何精度。采用2D高斯面元作为地图表示，增强表面匹配；提出新方法将激光雷达点云转换为平面约束的多模态高斯混合模型（GMM），在初始化与优化阶段提供持续且充分的监督，缓解过拟合风险；同时利用GMM进行网格提取，消除伪影，提升整体几何质量。实验表明，本方法在大场景3D重建中优于现有最优技术，相比基于激光雷达的方法精度提升52.6%，相比基于高斯的方法提升68.7%。
原文摘要 · Abstract (English)
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.
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