Surface Reconstruction from Gaussian Splatting via Novel Stereo Views
Yaniv Wolf Amit Bracha Ron Kimmel
The Gaussian splatting for radiance field rendering method has recently
emerged as an efficient approach for accurate scene representation. It
optimizes the location, size, color, and shape of a cloud of 3D Gaussian
elements to visually match, after projection, or splatting, a set of given
images taken from various viewing directions. And yet, despite the proximity of
Gaussian elements to the shape boundaries, direct surface reconstruction of
objects in the scene is a challenge.
  We propose a novel approach for surface reconstruction from Gaussian
splatting models. Rather than relying on the Gaussian elements' locations as a
prior for surface reconstruction, we leverage the superior novel-view synthesis
capabilities of 3DGS. To that end, we use the Gaussian splatting model to
render pairs of stereo-calibrated novel views from which we extract depth
profiles using a stereo matching method. We then combine the extracted RGB-D
images into a geometrically consistent surface. The resulting reconstruction is
more accurate and shows finer details when compared to other methods for
surface reconstruction from Gaussian splatting models, while requiring
significantly less compute time compared to other surface reconstruction
methods.
  We performed extensive testing of the proposed method on in-the-wild scenes,
taken by a smartphone, showcasing its superior reconstruction abilities.
Additionally, we tested the proposed method on the Tanks and Temples benchmark,
and it has surpassed the current leading method for surface reconstruction from
Gaussian splatting models. Project page: https://gs2mesh.github.io/.