High-fidelity 3D Gaussian Inpainting: preserving multi-view consistency and photorealistic details

• URL: http://arxiv.org/abs/2507.18023v1
• Date: Thu, 24 Jul 2025 01:48:50 GMT
• Title: High-fidelity 3D Gaussian Inpainting: preserving multi-view consistency and photorealistic details
• Authors: Jun Zhou, Dinghao Li, Nannan Li, Mingjie Wang,
• Abstract summary: Inpainting 3D scenes remains a challenging task due to the inherent irregularity of 3D structures.<n>We propose a novel 3D Gaussian inpainting framework that reconstructs complete 3D scenes by leveraging sparse inpainted views.<n>Our approach outperforms existing state-of-the-art methods in both visual quality and view consistency.
• Score: 8.279171283542066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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