Perspective-aware 3D Gaussian Inpainting with Multi-view Consistency
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911206782009344 |
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| author | Cheng, Yuxin Huang, Binxiao Wu, Taiqiang Zhou, Wenyong Ding, Chenchen Liu, Zhengwu Chesi, Graziano Wong, Ngai |
| author_facet | Cheng, Yuxin Huang, Binxiao Wu, Taiqiang Zhou, Wenyong Ding, Chenchen Liu, Zhengwu Chesi, Graziano Wong, Ngai |
| contents | 3D Gaussian inpainting, a critical technique for numerous applications in virtual reality and multimedia, has made significant progress with pretrained diffusion models. However, ensuring multi-view consistency, an essential requirement for high-quality inpainting, remains a key challenge. In this work, we present PAInpainter, a novel approach designed to advance 3D Gaussian inpainting by leveraging perspective-aware content propagation and consistency verification across multi-view inpainted images. Our method iteratively refines inpainting and optimizes the 3D Gaussian representation with multiple views adaptively sampled from a perspective graph. By propagating inpainted images as prior information and verifying consistency across neighboring views, PAInpainter substantially enhances global consistency and texture fidelity in restored 3D scenes. Extensive experiments demonstrate the superiority of PAInpainter over existing methods. Our approach achieves superior 3D inpainting quality, with PSNR scores of 26.03 dB and 29.51 dB on the SPIn-NeRF and NeRFiller datasets, respectively, highlighting its effectiveness and generalization capability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_10993 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Perspective-aware 3D Gaussian Inpainting with Multi-view Consistency Cheng, Yuxin Huang, Binxiao Wu, Taiqiang Zhou, Wenyong Ding, Chenchen Liu, Zhengwu Chesi, Graziano Wong, Ngai Computer Vision and Pattern Recognition 3D Gaussian inpainting, a critical technique for numerous applications in virtual reality and multimedia, has made significant progress with pretrained diffusion models. However, ensuring multi-view consistency, an essential requirement for high-quality inpainting, remains a key challenge. In this work, we present PAInpainter, a novel approach designed to advance 3D Gaussian inpainting by leveraging perspective-aware content propagation and consistency verification across multi-view inpainted images. Our method iteratively refines inpainting and optimizes the 3D Gaussian representation with multiple views adaptively sampled from a perspective graph. By propagating inpainted images as prior information and verifying consistency across neighboring views, PAInpainter substantially enhances global consistency and texture fidelity in restored 3D scenes. Extensive experiments demonstrate the superiority of PAInpainter over existing methods. Our approach achieves superior 3D inpainting quality, with PSNR scores of 26.03 dB and 29.51 dB on the SPIn-NeRF and NeRFiller datasets, respectively, highlighting its effectiveness and generalization capability. |
| title | Perspective-aware 3D Gaussian Inpainting with Multi-view Consistency |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.10993 |