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.

翻译：辐射场在从稀疏输入视角合成新视角方面展现了卓越性能，但现有方法面临训练成本高和推理速度慢的问题。本文提出DNGaussian——一种基于三维高斯辐射场的深度正则化框架，以低成本实现实时、高质量的小样本新视角合成。我们的动机源于近期3D高斯溅射的高效表示和惊人质量，尽管当输入视角减少时会出现几何退化。在高斯辐射场中，我们发现场景几何的退化主要与高斯原语的定位相关，且可通过深度约束进行缓解。为此，我们提出硬深度正则化与软深度正则化，在粗粒度单目深度监督下恢复精确场景几何，同时保持细粒度颜色外观。为进一步优化几何重塑的细节，引入全局-局部深度归一化增强对小范围局部深度变化的关注。在LLFF、DTU和Blender数据集上的大量实验表明，DNGaussian优于现有最优方法，在显著降低内存消耗、减少25倍训练时间和超过3000倍渲染速度的同时，达到可比或更优的结果。