  The 3D Gaussian Splatting technique has significantly advanced the
construction of radiance fields from multi-view images, enabling real-time
rendering. While point-based rasterization effectively reduces computational
demands for rendering, it often struggles to accurately reconstruct the
geometry of the target object, especially under strong lighting. To address
this challenge, we introduce a novel approach that combines octree-based
implicit surface representations with Gaussian splatting. Our method consists
of four stages. Initially, it reconstructs a signed distance field (SDF) and a
radiance field through volume rendering, encoding them in a low-resolution
octree. The initial SDF represents the coarse geometry of the target object.
Subsequently, it introduces 3D Gaussians as additional degrees of freedom,
which are guided by the SDF. In the third stage, the optimized Gaussians
further improve the accuracy of the SDF, allowing it to recover finer geometric
details compared to the initial SDF obtained in the first stage. Finally, it
adopts the refined SDF to further optimize the 3D Gaussians via splatting,
eliminating those that contribute little to visual appearance. Experimental
results show that our method, which leverages the distribution of 3D Gaussians
with SDFs, reconstructs more accurate geometry, particularly in images with
specular highlights caused by strong lighting.
