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-degree scenes from sparse training views. We 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 6.4% in LPIPS and by 12.2% in PSNR, and NeRF-based methods by at least 17.6% in LPIPS on the MipNeRF-360 dataset with substantially less training and inference cost.

翻译：近期，随着神经辐射场（NeRF）及其他隐式场景表示方法的提出，新视角合成问题受到广泛关注。最新进展——三维高斯溅射（3DGS）利用显式表示实现了高质量实时渲染。然而，3DGS仍需大量训练视角才能生成连贯的场景表示。在少样本设定下，类似NeRF，3DGS易对训练视角过拟合，导致背景坍塌与过量漂浮物，尤其是当训练视角数量减少时尤为显著。我们提出一种方法，能从稀疏训练视角训练基于3DGS的360°场景连贯辐射场。通过融合深度先验与生成性及显式约束，减少背景坍塌、消除漂浮物，并增强未见视角的连贯性。实验表明，本方法在MipNeRF-360数据集上相比基础3DGS，LPIPS提升6.4%、PSNR提升12.2%，相较基于NeRF的方法LPIPS至少提升17.6%，且训练与推理成本显著降低。