DN-Splatter: Depth and Normal Priors for Gaussian Splatting and Meshing
Matias Turkulainen Xuqian Ren Iaroslav Melekhov Otto Seiskari Esa Rahtu Juho Kannala
3D Gaussian splatting, a novel differentiable rendering technique, has
achieved state-of-the-art novel view synthesis results with high rendering
speeds and relatively low training times. However, its performance on scenes
commonly seen in indoor datasets is poor due to the lack of geometric
constraints during optimization. We extend 3D Gaussian splatting with depth and
normal cues to tackle challenging indoor datasets and showcase techniques for
efficient mesh extraction, an important downstream application. Specifically,
we regularize the optimization procedure with depth information, enforce local
smoothness of nearby Gaussians, and use the geometry of the 3D Gaussians
supervised by normal cues to achieve better alignment with the true scene
geometry. We improve depth estimation and novel view synthesis results over
baselines and show how this simple yet effective regularization technique can
be used to directly extract meshes from the Gaussian representation yielding
more physically accurate reconstructions on indoor scenes. Our code will be
released in https://github.com/maturk/dn-splatter.