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.
三维高斯Splatting是一种新颖的可微渲染技术，已在新视角合成结果上达到了最先进的水平，具有高渲染速度和相对较低的训练时间。然而，由于在优化过程中缺乏几何约束，其在常见的室内数据集场景中的表现不佳。我们通过深度和法线线索扩展了三维高斯Splatting，以应对具有挑战性的室内数据集，并展示了有效的网格提取技术，这是一个重要的下游应用。具体来说，我们用深度信息规范化优化程序，强制执行附近高斯的局部平滑性，并利用由法线线索监督的三维高斯的几何性质，以更好地与真实场景几何对齐。我们改善了深度估计和新视角合成结果，超越了基准线，并展示了这种简单而有效的规范化技术如何被用于直接从高斯表示中提取网格，从而在室内场景中获得更物理精确的重建。