Recent advances in 3D Gaussian Splatting (3DGS) have enabled real-time, photorealistic scene reconstruction. However, conventional 3DGS frameworks typically rely on sparse point clouds derived from Structure-from-Motion (SfM), which inherently suffer from scale ambiguity, limited geometric consistency, and strong view dependency due to the lack of geometric priors. In this work, a LiDAR-centric 3D Gaussian Splatting framework is proposed that explicitly incorporates metric geometric priors into the entire Gaussian optimization process. Instead of treating LiDAR data as a passive initialization source, 3DGS optimization is reformulated as a geometry-conditioned allocation and refinement problem under a fixed representational budget. Specifically, this work introduces (i) a geometry-texture-aware allocation strategy that selectively assigns Gaussian primitives to regions with high structural or appearance complexity, (ii) a curvature-adaptive refinement mechanism that dynamically guides Gaussian splitting toward geometrically complex areas during training, and (iii) a confidence-aware metric depth regularization that anchors the reconstructed geometry to absolute scale using LiDAR measurements while maintaining optimization stability. Extensive experiments on the ScanNet++ dataset and a custom real-world dataset validate the proposed approach. The results demonstrate state-of-the-art performance in metric-scale reconstruction with high geometric fidelity.

翻译：三维高斯泼溅（3DGS）的最新进展实现了实时、照片级逼真的场景重建。然而，传统的三维高斯泼溅框架通常依赖从运动恢复结构（SfM）导出的稀疏点云，此类点云天然存在尺度模糊、几何一致性有限以及因缺乏几何先验导致的强视角依赖性。本文提出一种以激光雷达（LiDAR）为中心的三维高斯泼溅框架，明确地将度量几何先验引入整个高斯优化过程。该框架不再将LiDAR数据视为被动的初始化源，而是将三维高斯泼溅优化重构为固定表示预算下以几何为条件的分配与精化问题。具体而言，本文提出：（i）一种几何-纹理感知分配策略，可选择性将高斯基元分配至结构或外观复杂度高的区域；（ii）一种曲率自适应精化机制，在训练过程中动态引导高斯分裂朝向几何复杂区域；（iii）一种置信度感知的度量深度正则化方法，利用LiDAR测量将重建几何锚定于绝对尺度，同时保持优化稳定性。在ScanNet++数据集及自建真实数据集上的大量实验验证了所提方法的有效性。结果表明，该方法在度量尺度重建中实现了几何保真度最高的最先进性能。