用一个模型实现任意缩放的3D高分辨率渲染，兼顾质量与速度。
Arbitrary-Scale 3D Gaussian Super-Resolution
- 引入尺度感知渲染与生成先验优化，支持任意缩放因子
- 相比原版3DGS提升6.59 dB PSNR，1080p下保持85帧/秒
- 适用于需要灵活缩放且资源受限的实时3D场景应用
现有3D高斯泼溅（3DGS）超分辨率方法通常仅支持固定倍数的高分辨率渲染，在资源受限场景中不实用。直接使用原始3DGS进行任意尺度的高分辨率视图渲染会因缺乏尺度感知能力而产生混叠伪影；而为3DGS添加后处理上采器则使框架复杂化并降低渲染效率。为此，我们构建了一个集成框架，融合尺度感知渲染、生成先验引导优化和渐进式超分辨机制，仅用一个3D模型即可实现任意缩放因子下的3D高斯超分辨率。特别地，该方法支持整数与非整数尺度渲染，灵活性更高。大量实验表明，该模型在单个模型下可生成高质量任意尺度高分辨率视图（相较3DGS提升6.59 dB PSNR），保持低分辨率视图与多尺度间结构一致性，同时维持实时渲染速度（1080p下达85 FPS）。
原文摘要 · Abstract (English)
Existing 3D Gaussian Splatting (3DGS) super-resolution methods typically perform high-resolution (HR) rendering of fixed scale factors, making them impractical for resource-limited scenarios. Directly rendering arbitrary-scale HR views with vanilla 3DGS introduces aliasing artifacts due to the lack of scale-aware rendering ability, while adding a post-processing upsampler for 3DGS complicates the framework and reduces rendering efficiency. To tackle these issues, we build an integrated framework that incorporates scale-aware rendering, generative prior-guided optimization, and progressive super-resolving to enable 3D Gaussian super-resolution of arbitrary scale factors with a single 3D model. Notably, our approach supports both integer and non-integer scale rendering to provide more flexibility. Extensive experiments demonstrate the effectiveness of our model in rendering high-quality arbitrary-scale HR views (6.59 dB PSNR gain over 3DGS) with a single model. It preserves structural consistency with LR views and across different scales, while maintaining real-time rendering speed (85 FPS at 1080p).
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