用可靠深度先验提升单目3D高斯点云渲染质量
In Depth We Trust: Reliable Monocular Depth Supervision for Gaussian Splatting
- 引入弱对齐深度变化学习，缓解单目深度歧义问题
- 选择性正则化不良几何区域，避免错误深度传播
- 适配多种模型和数据集，显著提升渲染真实感
在3D高斯点云渲染中使用精确的深度先验可缓解稀疏训练数据和无纹理表面带来的伪影。然而，获取准确深度图需专用采集设备。基础单目深度估计模型成本低，但存在尺度模糊、多视图不一致及局部几何误差，直接应用会降低渲染效果。本文提出一种将模糊且含噪的深度先验融入几何监督的训练框架，强调从弱对齐深度变化中学习的重要性。提出方法可识别病态几何区域，实现选择性单目深度正则化，限制错误深度向良好重建结构传播。在多个数据集上的实验表明，该方法在不同高斯点云变体和单目深度骨干网络下均显著提升几何精度，实现更真实的深度估计与更高渲染质量。
原文摘要 · Abstract (English)
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.
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