  Recently, 3D Gaussian Splatting (3DGS) has emerged as an efficient approach
for accurately representing scenes. However, despite its superior novel view
synthesis capabilities, extracting the geometry of the scene directly from the
Gaussian properties remains a challenge, as those are optimized based on a
photometric loss. While some concurrent models have tried adding geometric
constraints during the Gaussian optimization process, they still produce noisy,
unrealistic surfaces.
  We propose a novel approach for bridging the gap between the noisy 3DGS
representation and the smooth 3D mesh representation, by injecting real-world
knowledge into the depth extraction process. Instead of extracting the geometry
of the scene directly from the Gaussian properties, we instead extract the
geometry through a pre-trained stereo-matching model. We render stereo-aligned
pairs of images corresponding to the original training poses, feed the pairs
into a stereo model to get a depth profile, and finally fuse all of the
profiles together to get a single mesh.
  The resulting reconstruction is smoother, more accurate and shows more
intricate details compared to other methods for surface reconstruction from
Gaussian Splatting, while only requiring a small overhead on top of the fairly
short 3DGS optimization process.
  We performed extensive testing of the proposed method on in-the-wild scenes,
obtained using a smartphone, showcasing its superior reconstruction abilities.
Additionally, we tested the method on the Tanks and Temples and DTU benchmarks,
achieving state-of-the-art results.
