SplatFill：基于深度引导的高斯泼溅的3D场景修复
SplatFill: 3D Scene Inpainting via Depth-Guided Gaussian Splatting
AI总结：
提出深度引导的3DGS修复方法SplatFill，结合深度与物体联合监督及一致性感知细化，在SPIn-NeRF数据集上实现更优视觉质量并减少24.5%训练时间。
摘要：
3D高斯泼溅（3DGS）已能够从多视图图像集合中创建高度逼真的3D场景表示。然而，修复缺失区域（无论是由于遮挡还是场景编辑所致）仍然是一项具有挑战性的任务，常常导致细节模糊、伪影和几何不一致。在这项工作中，我们引入了SplatFill，一种新颖的深度引导的3DGS场景修复方法，实现了最先进的感知质量和更高的效率。我们的方法结合了两个关键思想：（1）基于深度和基于对象的联合监督，以确保修复的高斯在3D空间中准确放置并与周围几何对齐；（2）我们提出了一种一致性感知的细化方案，该方案选择性地识别并纠正不一致区域，而不会干扰场景的其余部分。在SPIn-NeRF数据集上的评估表明，SplatFill不仅在视觉保真度上超越了现有的基于NeRF和基于3DGS的修复方法，而且还将训练时间减少了24.5%。定性结果显示，我们的方法在具有挑战性的视角下提供了更清晰的细节、更少的伪影和更强的一致性。
英文摘要：
3D Gaussian Splatting (3DGS) has enabled the creation of highly realistic 3D scene representations from sets of multi-view images. However, inpainting missing regions, whether due to occlusion or scene editing, remains a challenging task, often leading to blurry details, artifacts, and inconsistent geometry. In this work, we introduce SplatFill, a novel depth-guided approach for 3DGS scene inpainting that achieves state-of-the-art perceptual quality and improved efficiency. Our method combines two key ideas: (1) joint depth-based and object-based supervision to ensure inpainted Gaussians are accurately placed in 3D space and aligned with surrounding geometry, and (2) we propose a consistency-aware refinement scheme that selectively identifies and corrects inconsistent regions without disrupting the rest of the scene. Evaluations on the SPIn-NeRF dataset demonstrate that SplatFill not only surpasses existing NeRF-based and 3DGS-based inpainting methods in visual fidelity but also reduces training time by 24.5%. Qualitative results show our method delivers sharper details, fewer artifacts, and greater coherence across challenging viewpoints.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
