用3D高斯点云实现跨视角一致的图像超分辨率，无需微调。
Bridging Diffusion Models and 3D Representations: A 3D Consistent Super-Resolution Framework
- 基于3D高斯点云表示，融合现成2D扩散超分模型。
- 在MipNeRF360和LLFF数据集上实现高质量且结构一致的3D重建。
- 适合需要高保真3D内容生成的研究者和开发者。
我们提出3D超分辨率（3DSR），一种基于3D高斯点云的新型超分辨率框架，利用现成的基于扩散模型的2D超分辨率方法。3DSR通过显式的3D高斯点云场景表示，确保多视角间的一致性，区别于以往仅关注图像上采样或隐式引入3D一致性的方法。该方法在不进行额外微调的情况下提升视觉质量，同时保证重建场景内的空间一致性。我们在MipNeRF360和LLFF数据集上进行了评估，结果表明3DSR能生成视觉上令人信服的高分辨率输出，并保持3D重建的结构一致性。
原文摘要 · Abstract (English)
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.
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