  Recently, 3D Gaussian Splatting(3DGS) has revolutionized neural rendering
with its high-quality rendering and real-time speed. However, when it comes to
indoor scenes with a significant number of textureless areas, 3DGS yields
incomplete and noisy reconstruction results due to the poor initialization of
the point cloud and under-constrained optimization. Inspired by the continuity
of signed distance field (SDF), which naturally has advantages in modeling
surfaces, we present a unified optimizing framework integrating neural SDF with
3DGS. This framework incorporates a learnable neural SDF field to guide the
densification and pruning of Gaussians, enabling Gaussians to accurately model
scenes even with poor initialized point clouds. At the same time, the geometry
represented by Gaussians improves the efficiency of the SDF field by piloting
its point sampling. Additionally, we regularize the optimization with normal
and edge priors to eliminate geometry ambiguity in textureless areas and
improve the details. Extensive experiments in ScanNet and ScanNet++ show that
our method achieves state-of-the-art performance in both surface reconstruction
and novel view synthesis.
