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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.04090 |
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| _version_ | 1866918191474671616 |
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| author | Chen, Yi-Ting Liao, Ting-Hsuan Guo, Pengsheng Schwing, Alexander Huang, Jia-Bin |
| author_facet | Chen, Yi-Ting Liao, Ting-Hsuan Guo, Pengsheng Schwing, Alexander Huang, Jia-Bin |
| contents | We propose 3D Super Resolution (3DSR), a novel 3D Gaussian-splatting-based super-resolution framework that leverages off-the-shelf diffusion-based 2D super-resolution models. 3DSR encourages 3D consistency across views via the use of an explicit 3D Gaussian-splatting-based scene representation. This makes the proposed 3DSR different from prior work, such as image upsampling or the use of video super-resolution, which either don't consider 3D consistency or aim to incorporate 3D consistency implicitly. Notably, our method enhances visual quality without additional fine-tuning, ensuring spatial coherence within the reconstructed scene. We evaluate 3DSR on MipNeRF360 and LLFF data, demonstrating that it produces high-resolution results that are visually compelling, while maintaining structural consistency in 3D reconstructions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_04090 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bridging Diffusion Models and 3D Representations: A 3D Consistent Super-Resolution Framework Chen, Yi-Ting Liao, Ting-Hsuan Guo, Pengsheng Schwing, Alexander Huang, Jia-Bin Computer Vision and Pattern Recognition We propose 3D Super Resolution (3DSR), a novel 3D Gaussian-splatting-based super-resolution framework that leverages off-the-shelf diffusion-based 2D super-resolution models. 3DSR encourages 3D consistency across views via the use of an explicit 3D Gaussian-splatting-based scene representation. This makes the proposed 3DSR different from prior work, such as image upsampling or the use of video super-resolution, which either don't consider 3D consistency or aim to incorporate 3D consistency implicitly. Notably, our method enhances visual quality without additional fine-tuning, ensuring spatial coherence within the reconstructed scene. We evaluate 3DSR on MipNeRF360 and LLFF data, demonstrating that it produces high-resolution results that are visually compelling, while maintaining structural consistency in 3D reconstructions. |
| title | Bridging Diffusion Models and 3D Representations: A 3D Consistent Super-Resolution Framework |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.04090 |