深入信赖：为高斯散射可靠的单目深度监督
In Depth We Trust: Reliable Monocular Depth Supervision for Gaussian Splatting
AI总结：
本文提出一种整合模糊和噪声深度先验的训练框架，以提升高斯散射的几何监督，从而提高渲染质量。
摘要：
在3D高斯散射中使用准确的深度先验有助于缓解稀疏训练数据和无纹理表面导致的伪影。然而，获取准确深度图需要专用系统。基础单目深度估计模型提供了一种经济替代方案，但存在尺度模糊、多视图不一致和局部几何不准确的问题，这些在直接应用时会降低渲染性能。本文针对可靠利用单目深度先验进行高斯散射（GS）渲染增强的挑战，提出一种训练框架，将模糊且噪声的深度先验整合到几何监督中。我们强调了从弱对齐深度变化中学习的重要性。我们引入一种方法来隔离不稳定的几何结构，以选择性地对单目深度进行正则化，限制深度不准确性的传播到良好重建的3D结构中。在多样本集上进行的大量实验显示，几何精度有显著提高，导致在不同GS变体和单目深度基座测试中获得更忠实的深度估计和更高的渲染质量。
英文摘要：
Using accurate depth priors in 3D Gaussian Splatting helps mitigate artifacts caused by sparse training data and textureless surfaces. However, acquiring accurate depth maps requires specialized acquisition systems. Foundation monocular depth estimation models offer a cost-effective alternative, but they suffer from scale ambiguity, multi-view inconsistency, and local geometric inaccuracies, which can degrade rendering performance when applied naively. This paper addresses the challenge of reliably leveraging monocular depth priors for Gaussian Splatting (GS) rendering enhancement. To this end, we introduce a training framework integrating scale-ambiguous and noisy depth priors into geometric supervision. We highlight the importance of learning from weakly aligned depth variations. We introduce a method to isolate ill-posed geometry for selective monocular depth regularization, restricting the propagation of depth inaccuracies into well-reconstructed 3D structures. Extensive experiments across diverse datasets show consistent improvements in geometric accuracy, leading to more faithful depth estimation and higher rendering quality across different GS variants and monocular depth backbones tested.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
