Depth-guided 3D reconstruction has gained popularity as a fast alternative to optimization-heavy approaches, yet existing methods still suffer from scale drift, multi-view inconsistencies, and the need for substantial refinement to achieve high-fidelity geometry. Here, we propose SwiftNDC, a fast and general framework built around a Neural Depth Correction field that produces cross-view consistent depth maps. From these refined depths, we generate a dense point cloud through back-projection and robust reprojection-error filtering, obtaining a clean and uniformly distributed geometric initialization for downstream reconstruction. This reliable dense geometry substantially accelerates 3D Gaussian Splatting (3DGS) for mesh reconstruction, enabling high-quality surfaces with significantly fewer optimization iterations. For novel-view synthesis, SwiftNDC can also improve 3DGS rendering quality, highlighting the benefits of strong geometric initialization. We conduct a comprehensive study across five datasets, including two for mesh reconstruction, as well as three for novel-view synthesis. SwiftNDC consistently reduces running time for accurate mesh reconstruction and boosts rendering fidelity for view synthesis, demonstrating the effectiveness of combining neural depth refinement with robust geometric initialization for high-fidelity and efficient 3D reconstruction.

翻译：深度引导的三维重建作为一种快速替代计算密集型优化方法的技术已获得广泛关注，但现有方法仍存在尺度漂移、多视角不一致以及需要大量细化才能实现高保真几何结构的问题。本文提出SwiftNDC——一个围绕神经深度校正场构建的快速通用框架，该框架能够生成跨视角一致的深度图。通过这些优化后的深度，我们通过反向投影和鲁棒的重投影误差滤波生成稠密点云，为下游重建提供干净且均匀分布的几何初始化。这种可靠的稠密几何结构显著加速了用于网格重建的三维高斯泼溅（3DGS）过程，能以更少的优化迭代次数获得高质量表面。对于新视角合成任务，SwiftNDC同样能提升3DGS的渲染质量，这凸显了强几何初始化的优势。我们在五个数据集上进行了全面研究，其中两个用于网格重建，三个用于新视角合成。SwiftNDC在精确网格重建方面持续减少运行时间，并在视角合成中提升渲染保真度，这证明了神经深度优化与鲁棒几何初始化相结合对于实现高保真高效三维重建的有效性。