Radiance fields have demonstrated impressive performance in synthesizing
novel views from sparse input views, yet prevailing methods suffer from high
training costs and slow inference speed. This paper introduces DNGaussian, a
depth-regularized framework based on 3D Gaussian radiance fields, offering
real-time and high-quality few-shot novel view synthesis at low costs. Our
motivation stems from the highly efficient representation and surprising
quality of the recent 3D Gaussian Splatting, despite it will encounter a
geometry degradation when input views decrease. In the Gaussian radiance
fields, we find this degradation in scene geometry primarily lined to the
positioning of Gaussian primitives and can be mitigated by depth constraint.
Consequently, we propose a Hard and Soft Depth Regularization to restore
accurate scene geometry under coarse monocular depth supervision while
maintaining a fine-grained color appearance. To further refine detailed
geometry reshaping, we introduce Global-Local Depth Normalization, enhancing
the focus on small local depth changes. Extensive experiments on LLFF, DTU, and
Blender datasets demonstrate that DNGaussian outperforms state-of-the-art
methods, achieving comparable or better results with significantly reduced
memory cost, a $25 \times$ reduction in training time, and over $3000 \times$
faster rendering speed.

DNGaussian 是一种基于 3D 高斯辐射场的深度正则化框架，能够以较低成本实时高质量地进行少样本新视角合成，通过硬和软深度正则化以及全局 - 局部深度归一化等方法，恢复精确的场景几何和细节几何重塑，性能优于现有方法。