The problem of novel view synthesis has grown significantly in popularity
recently with the introduction of Neural Radiance Fields (NeRFs) and other
implicit scene representation methods. A recent advance, 3D Gaussian Splatting
(3DGS), leverages an explicit representation to achieve real-time rendering
with high-quality results. However, 3DGS still requires an abundance of
training views to generate a coherent scene representation. In few shot
settings, similar to NeRF, 3DGS tends to overfit to training views, causing
background collapse and excessive floaters, especially as the number of
training views are reduced. We propose a method to enable training coherent
3DGS-based radiance fields of 360 scenes from sparse training views. We find
that using naive depth priors is not sufficient and integrate depth priors with
generative and explicit constraints to reduce background collapse, remove
floaters, and enhance consistency from unseen viewpoints. Experiments show that
our method outperforms base 3DGS by up to 30.5% and NeRF-based methods by up to
15.6% in LPIPS on the MipNeRF-360 dataset with substantially less training and
inference cost.

我们提出了一种从稀疏训练视角中训练一致的基于 3DGS 的辐射场的方法，通过集成深度先验、生成和显式约束来减少背景折叠、移除浮点值，并增强来自未见视角的一致性，实验证明我们的方法在 MipNeRF-360 数据集上以较少的训练和推理成本超过了基本的 3DGS 的 30.5% 和基于 NeRF 的方法的 15.6%。