  High-fidelity 3D reconstruction of common indoor scenes is crucial for VR and
AR applications. 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. Specifically, we regularize the optimization
procedure with depth information, enforce local smoothness of nearby Gaussians,
and use off-the-shelf monocular networks to achieve better alignment with the
true scene geometry. We propose an adaptive depth loss based on the gradient of
color images, improving depth estimation and novel view synthesis results over
various baselines. Our simple yet effective regularization technique enables
direct mesh extraction from the Gaussian representation, yielding more
physically accurate reconstructions of indoor scenes. Our code will be released
in https://github.com/maturk/dn-splatter.
