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.
3D Gaussian Splatting (GS) 在准确表示底层3D场景几何方面面临显著挑战，导致深度图渲染时出现不准确和浮动伪影。为了解决这一局限性，本文进行了全面分析，研究了在高斯基元优化过程中整合深度先验的方法，并提出了一种新策略。该策略动态利用来自现成的立体网络的深度线索，在训练过程中处理由GS模型本身渲染的虚拟立体对，从而实现场景表示的一致自我改进。我们在三个流行数据集上的实验结果验证了这一发现，这是首次对这些模型的深度准确性进行评估的研究。