3D Gaussian splatting has demonstrated impressive performance in real-time
novel view synthesis. However, achieving successful reconstruction from RGB
images generally requires multiple input views captured under static
conditions. To address the challenge of sparse input views, previous approaches
have incorporated depth supervision into the training of 3D Gaussians to
mitigate overfitting, using dense predictions from pretrained depth networks as
pseudo-ground truth. Nevertheless, depth predictions from monocular depth
estimation models inherently exhibit significant uncertainty in specific areas.
Relying solely on pixel-wise L2 loss may inadvertently incorporate detrimental
noise from these uncertain areas. In this work, we introduce a novel method to
supervise the depth distribution of 3D Gaussians, utilizing depth priors with
integrated uncertainty estimates. To address these localized errors in depth
predictions, we integrate a patch-wise optimal transport strategy to complement
traditional L2 loss in depth supervision. Extensive experiments conducted on
the LLFF, DTU, and Blender datasets demonstrate that our approach, UGOT,
achieves superior novel view synthesis and consistently outperforms
state-of-the-art methods.

3D 高斯 splatting 在实时新视图合成中表现出令人印象深刻的性能。本论文提出了一种新颖的方法，利用具有集成不确定性估计的深度先验来监督 3D 高斯分布，以解决稀疏输入视图的挑战，并集成了一个基于补丁的最优运输策略来补充传统 L2 损失进行深度监督，通过在 LLFF、DTU 和 Blender 数据集上进行广泛实验，证明了我们的方法 UGOT 在新视图合成方面取得了卓越成果，并始终优于现有方法。