Embodied intelligence requires precise reconstruction and rendering to simulate large-scale real-world data. Although 3D Gaussian Splatting (3DGS) has recently demonstrated high-quality results with real-time performance, it still faces challenges in indoor scenes with large, textureless regions, resulting in incomplete and noisy reconstructions due to poor point cloud initialization and underconstrained optimization. Inspired by the continuity of signed distance field (SDF), which naturally has advantages in modeling surfaces, we propose a unified optimization framework that integrates neural signed distance fields (SDFs) with 3DGS for accurate geometry reconstruction and real-time rendering. This framework incorporates a neural SDF field to guide the densification and pruning of Gaussians, enabling Gaussians to model scenes accurately even with poor initialized point clouds. Simultaneously, the geometry represented by Gaussians improves the efficiency of the SDF field by piloting its point sampling. Additionally, we introduce two regularization terms based on normal and edge priors to resolve geometric ambiguities in textureless areas and enhance detail accuracy. Extensive experiments in ScanNet and ScanNet++ show that our method achieves state-of-the-art performance in both surface reconstruction and novel view synthesis.

翻译：具身智能需要精确的三维重建与渲染来模拟大规模真实世界数据。尽管3D高斯泼溅（3DGS）近期已展现出实时渲染的高质量成果，但在具有大面积无纹理区域的室内场景中仍面临挑战——由于点云初始化质量不佳及优化约束不足，常导致重建结果残缺且含有噪点。受符号距离场（SDF）在表面建模中天然具备连续性的启发，我们提出一种将神经符号距离场（SDF）与3DGS相融合的统一优化框架，以实现精确几何重建与实时渲染。该框架引入神经SDF场来指导高斯体的致密化与剪枝操作，使高斯体即使在初始点云质量较差时仍能准确建模场景。同时，高斯体表征的几何结构通过引导SDF场的点采样，提升了SDF场的计算效率。此外，我们基于法向量与边缘先验引入两项正则化项，以解决无纹理区域的几何歧义问题并增强细节精度。在ScanNet与ScanNet++数据集上的大量实验表明，本方法在表面重建与新视角合成任务上均达到了最先进的性能水平。