DN-Splatter: Depth and Normal Priors for Gaussian Splatting and Meshing

Matias Turkulainen, Xuqian Ren, Iaroslav Melekhov, Otto Seiskari, Esa Rahtu, Juho Kannala

Abstract: High-fidelity 3D reconstruction of common indoor scenes is crucial for VR and AR applications. 3D Gaussian splat-ting, a novel differentiable rendering technique, has achieved state-of-the-art novel view synthesis results with high ren-dering 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 op-timization. In this work, we explore the use of readily accessible geometric cues to enhance Gaussian splatting op-timization in challenging, ill-posed, and textureless scenes. 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 align-ment 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 Gaus-sian representation, yielding more physically accurate re-constructions of indoor scenes. Our code will be released in https://github.com/maturk/dn-splatter.

External IDs:dblp:conf/wacv/TurkulainenRMSR25