Abstract
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.
Abstract (translated)
3D高斯点阵（3DGS）技术使得从多视角图像集合中创建高度逼真的3D场景表示成为可能。然而，填补由于遮挡或场景编辑导致的缺失区域仍然是一个艰巨的任务，这通常会导致模糊细节、伪影以及不一致的几何结构问题。在这项工作中，我们推出了SplatFill，这是一种新颖的基于深度引导的方法，用于解决3DGS场景中的缺口填充问题，它在感知质量和效率提升方面均达到了行业领先水平。我们的方法结合了两个关键理念：(1) 基于联合深度和对象监督的策略，确保填补的高斯点阵能够准确地放置在三维空间中，并且与周围几何结构对齐；(2) 我们提出了一种一致性感知的细化方案，能够有选择性地识别并修正不一致区域，而不会干扰场景的其他部分。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3DGS的缺口填充方法，而且还减少了24.5%的训练时间。定性结果显示我们的方法能够提供更清晰的细节、减少伪影，并且在具有挑战性的视角下表现出更好的一致性。
URL
https://arxiv.org/abs/2509.07809
