Recent advancements in multi-view 3D reconstruction and novel-view synthesis, particularly through Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), have greatly enhanced the fidelity and efficiency of 3D content creation. However, inpainting 3D scenes remains a challenging task due to the inherent irregularity of 3D structures and the critical need for maintaining multi-view consistency. In this work, we propose a novel 3D Gaussian inpainting framework that reconstructs complete 3D scenes by leveraging sparse inpainted views. Our framework incorporates an automatic Mask Refinement Process and region-wise Uncertainty-guided Optimization. Specifically, we refine the inpainting mask using a series of operations, including Gaussian scene filtering and back-projection, enabling more accurate localization of occluded regions and realistic boundary restoration. Furthermore, our Uncertainty-guided Fine-grained Optimization strategy, which estimates the importance of each region across multi-view images during training, alleviates multi-view inconsistencies and enhances the fidelity of fine details in the inpainted results. Comprehensive experiments conducted on diverse datasets demonstrate that our approach outperforms existing state-of-the-art methods in both visual quality and view consistency.
多视图三维重建与新视角合成的最新进展，尤其是通过神经辐射场（Neural Radiance Fields, NeRF）和三维高斯点渲染（3D Gaussian Splatting, 3DGS），极大提升了三维内容创作的保真度与效率。然而，由于三维结构固有的不规则性，以及保持多视图一致性的关键需求，三维场景修补（inpainting）依然是一项具有挑战性的任务。本文提出了一种新颖的三维高斯修补框架，通过利用稀疏的修补视图来重建完整的三维场景。该框架引入了自动化掩码优化流程（Mask Refinement Process）与基于区域的不确定性感知优化（Uncertainty-guided Optimization）。具体而言，我们通过一系列操作（包括高斯场景滤波与反向投影）来优化修补掩码，从而更准确地定位被遮挡区域并真实恢复边界。此外，我们提出的不确定性感知细粒度优化策略（Uncertainty-guided Fine-grained Optimization），在训练过程中估计多视图图像中各区域的重要性，从而缓解多视图不一致问题，并提升修补结果在细节上的保真度。在多个数据集上的综合实验表明，我们的方法在视觉质量与视角一致性方面均优于现有的最先进方法。