In this paper, we present a method to optimize Gaussian splatting with a limited number of images while avoiding overfitting. Representing a 3D scene by combining numerous Gaussian splats has yielded outstanding visual quality. However, it tends to overfit the training views when only a small number of images are available. To address this issue, we introduce a dense depth map as a geometry guide to mitigate overfitting. We obtained the depth map using a pre-trained monocular depth estimation model and aligning the scale and offset using sparse COLMAP feature points. The adjusted depth aids in the color-based optimization of 3D Gaussian splatting, mitigating floating artifacts, and ensuring adherence to geometric constraints. We verify the proposed method on the NeRF-LLFF dataset with varying numbers of few images. Our approach demonstrates robust geometry compared to the original method that relies solely on images. Project page: robot0321.github.io/DepthRegGS

翻译：本文提出了一种方法，能够在图像数量有限的情况下优化高斯泼溅，同时避免过拟合。通过组合大量高斯泼溅来表示三维场景已展现出卓越的视觉质量。然而，当仅使用少量图像时，该方法容易对训练视角产生过拟合。为解决这一问题，我们引入稠密深度图作为几何引导以缓解过拟合。我们利用预训练的单目深度估计模型获取深度图，并通过稀疏COLMAP特征点对齐尺度与偏移量。调整后的深度有助于三维高斯泼溅的基于颜色优化，减少浮动伪影并确保几何约束的遵循。我们在NeRF-LLFF数据集上验证了所提方法在不同少样本图像配置下的性能。相较于仅依赖图像的原始方法，本方法展现了更鲁棒的几何结构。项目页面：robot0321.github.io/DepthRegGS