CVJul 11, 2025

RePaintGS: Reference-Guided Gaussian Splatting for Realistic and View-Consistent 3D Scene Inpainting

arXiv:2507.08434v1
Originality Incremental advance
AI Analysis

This addresses the issue of view inconsistency in 3D scene editing for applications like virtual reality or content creation, but it is incremental as it builds on prior inpainting and blending techniques.

The paper tackles the problem of 3D scene inpainting for object removal in radiance field representations, where existing methods cause inconsistencies across viewpoints; the proposed RePaintGS method improves geometric fidelity and appearance consistency by leveraging a reference view to guide optimization.

Radiance field methods, such as Neural Radiance Field or 3D Gaussian Splatting, have emerged as seminal 3D representations for synthesizing realistic novel views. For practical applications, there is ongoing research on flexible scene editing techniques, among which object removal is a representative task. However, removing objects exposes occluded regions, often leading to unnatural appearances. Thus, studies have employed image inpainting techniques to replace such regions with plausible content - a task referred to as 3D scene inpainting. However, image inpainting methods produce one of many plausible completions for each view, leading to inconsistencies between viewpoints. A widely adopted approach leverages perceptual cues to blend inpainted views smoothly. However, it is prone to detail loss and can fail when there are perceptual inconsistencies across views. In this paper, we propose a novel 3D scene inpainting method that reliably produces realistic and perceptually consistent results even for complex scenes by leveraging a reference view. Given the inpainted reference view, we estimate the inpainting similarity of the other views to adjust their contribution in constructing an accurate geometry tailored to the reference. This geometry is then used to warp the reference inpainting to other views as pseudo-ground truth, guiding the optimization to match the reference appearance. Comparative evaluation studies have shown that our approach improves both the geometric fidelity and appearance consistency of inpainted scenes.

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