  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
