修复3D场景缺失区域，保持多视角一致性和真实细节。
High-fidelity 3D Gaussian Inpainting: preserving multi-view consistency and photorealistic details
- 用稀疏修复视图重建完整3D场景，自动优化掩码与不确定性。
- 多视角一致性提升，细节保真度显著优于现有方法。
- 适合需要高质量3D内容修复的视觉生成研究者。
近期基于神经辐射场（NeRF）和3D高斯溅射（3DGS）的多视角3D重建与新视角合成技术大幅提升了3D内容生成的保真度与效率。然而，由于3D结构固有的不规则性及对多视角一致性的严苛要求，3D场景修复仍具挑战。本文提出一种新型3D高斯修复框架，通过稀疏修复视图重建完整3D场景。框架引入自动掩码精化流程与区域级不确定性引导优化策略。具体而言，通过高斯场景过滤与反投影操作精炼修复掩码，实现遮挡区域更精准定位与自然边界恢复。同时，不确定性引导的细粒度优化策略在训练中估计各区域在多视角图像中的重要性，有效缓解多视角不一致问题，增强修复结果的精细细节保真度。在多个数据集上的全面实验表明，该方法在视觉质量与视角一致性方面均超越现有最先进方法。
原文摘要 · Abstract (English)
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.
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