3D Gaussian Splatting (GS) significantly struggles to accurately represent the underlying 3D scene geometry, resulting in inaccuracies and floating artifacts when rendering depth maps. In this paper, we address this limitation, undertaking a comprehensive analysis of the integration of depth priors throughout the optimization process of Gaussian primitives, and present a novel strategy for this purpose. This latter dynamically exploits depth cues from a readily available stereo network, processing virtual stereo pairs rendered by the GS model itself during training and achieving consistent self-improvement of the scene representation. Experimental results on three popular datasets, breaking ground as the first to assess depth accuracy for these models, validate our findings.

翻译：三维高斯溅射（GS）在准确表示底层三维场景几何方面存在显著困难，导致渲染深度图时出现不准确性和漂浮伪影。本文针对这一局限，深入分析了深度先验在高斯基元优化全过程中的整合方式，并提出一种用于此目的的新颖策略。该策略动态利用来自现成立体网络的深度线索，处理训练期间由GS模型自身渲染的虚拟立体图像对，从而实现场景表示的一致自我改进。在三个常用数据集上的实验结果——作为首个评估此类模型深度准确性的工作——验证了我们的发现。