Self-Ensembling Gaussian Splatting for Few-Shot Novel View Synthesis
This addresses a specific overfitting issue in 3D reconstruction for computer vision applications, representing a strong incremental improvement.
The paper tackles the problem of 3D Gaussian Splatting overfitting with sparse training views for novel view synthesis, and introduces Self-Ensembling Gaussian Splatting which improves synthesis quality under few-shot conditions, outperforming state-of-the-art methods on multiple datasets.
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 $\mathbfΔ$-model and a $\mathbfΣ$-model are jointly trained on the available images. The $\mathbfΔ$-model is dynamically perturbed based on rendering uncertainty across training steps, generating diverse perturbed models with negligible computational overhead. Discrepancies between the $\mathbfΣ$-model and these perturbed models are minimized throughout training, forming a robust ensemble of 3DGS models. This ensemble, represented by the $\mathbfΣ$-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.