SplatFill: 3D Scene Inpainting via Depth-Guided Gaussian Splatting

• URL: http://arxiv.org/abs/2509.07809v1
• Date: Tue, 09 Sep 2025 14:47:47 GMT
• Title: SplatFill: 3D Scene Inpainting via Depth-Guided Gaussian Splatting
• Authors: Mahtab Dahaghin, Milind G. Padalkar, Matteo Toso, Alessio Del Bue,
• Abstract summary: 3D Gaussian Splatting (3DGS) has enabled the creation of highly realistic 3D scene representations from sets of multi-view images.<n>In this work, we introduce SplatFill, a novel depth-guided approach for 3DGS scene inpainting that state-of-the-art perceptual quality and improved efficiency.
• Score: 17.45252036814217
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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