3D Gaussian Splatting (3DGS) has demonstrated remarkable effectiveness in novel view synthesis (NVS). However, 3DGS tends to overfit when trained with sparse views, limiting its generalization to novel viewpoints. In this paper, we address this overfitting issue by introducing Self-Ensembling Gaussian Splatting (SE-GS). We achieve self-ensembling by incorporating an uncertainty-aware perturbation strategy during training. A $\mathbf{\Delta}$-model and a $\mathbf{\Sigma}$-model are jointly trained on the available images. The $\mathbf{\Delta}$-model is dynamically perturbed based on rendering uncertainty across training steps, generating diverse perturbed models with negligible computational overhead. Discrepancies between the $\mathbf{\Sigma}$-model and these perturbed models are minimized throughout training, forming a robust ensemble of 3DGS models. This ensemble, represented by the $\mathbf{\Sigma}$-model, is then used to generate novel-view images during inference. Experimental results on the LLFF, Mip-NeRF360, DTU, and MVImgNet datasets demonstrate that our approach enhances NVS quality under few-shot training conditions, outperforming existing state-of-the-art methods. The code is released at: https://sailor-z.github.io/projects/SEGS.html.

翻译：三维高斯溅射（3DGS）在新视角合成（NVS）中已展现出显著的有效性。然而，当使用稀疏视角进行训练时，3DGS容易过拟合，限制了其对新视角的泛化能力。本文通过引入自集成高斯溅射（SE-GS）来解决这一过拟合问题。我们在训练过程中采用一种不确定性感知的扰动策略来实现自集成。一个$\mathbf{\Delta}$模型和一个$\mathbf{\Sigma}$模型在可用图像上联合训练。$\mathbf{\Delta}$模型根据训练步骤间的渲染不确定性进行动态扰动，以可忽略的计算开销生成多样化的扰动模型。在整个训练过程中，$\mathbf{\Sigma}$模型与这些扰动模型之间的差异被最小化，从而形成一个鲁棒的3DGS模型集成。该集成由$\mathbf{\Sigma}$模型表示，随后在推理阶段用于生成新视角图像。在LLFF、Mip-NeRF360、DTU和MVImgNet数据集上的实验结果表明，我们的方法在少样本训练条件下提升了NVS质量，优于现有的最先进方法。代码发布于：https://sailor-z.github.io/projects/SEGS.html。