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 this https URL

通过深度和法线线索来拓展 3D 高斯喷洒技术，以解决室内数据集中的挑战，并展示了有效的网格提取技术。

DN-Splatter: 高斯点片剖分和网格剖分的深度和法线先验

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

In recent years, neural implicit surface reconstruction methods have become
popular for multi-view 3D reconstruction. In contrast to traditional multi-view
stereo methods, these approaches tend to produce smoother and more complete
reconstructions due to the inductive smoothness bias of neural networks.
State-of-the-art neural implicit methods allow for high-quality reconstructions
of simple scenes from many input views. Yet, their performance drops
significantly for larger and more complex scenes and scenes captured from
sparse viewpoints. This is caused primarily by the inherent ambiguity in the
RGB reconstruction loss that does not provide enough constraints, in particular
in less-observed and textureless areas. Motivated by recent advances in the
area of monocular geometry prediction, we systematically explore the utility
these cues provide for improving neural implicit surface reconstruction. We
demonstrate that depth and normal cues, predicted by general-purpose monocular
estimators, significantly improve reconstruction quality and optimization time.
Further, we analyse and investigate multiple design choices for representing
neural implicit surfaces, ranging from monolithic MLP models over single-grid
to multi-resolution grid representations. We observe that geometric monocular
priors improve performance both for small-scale single-object as well as
large-scale multi-object scenes, independent of the choice of representation.

通过 monocular geometry prediction 预测的深度和法向量提示显着提高神经隐式表面重建的质量和优化时间，特别是在大规模复杂场景下，独立于表征的选择。