用变分扩散策略提升神经辐射场的视角一致超分辨率质量。
Advancing Super-Resolution in Neural Radiance Fields via Variational Diffusion Strategies
- 结合2D超分模型与变分得分蒸馏技术，精准微调图像生成。
- 在LLFF数据集上实现更高质量、视角一致的超分辨率结果。
- 适合关注3D渲染与图像生成融合的科研人员和工程师。
我们提出一种基于扩散引导的视图一致超分辨率方法，用于神经渲染。该方法利用现有2D超分辨率模型，结合变分得分蒸馏（VSD）和LoRA微调辅助模块，并采用空间训练策略，显著提升放大后2D图像的质量与一致性，优于文献中已有方法如DiSR-NeRF提出的Renoised Score Distillation（RSD）或DreamFusion中的SDS。VSD得分有助于对超分模型进行精确微调，生成高质量、视角一致的图像。为解决独立2D超分图像间常见的不一致性问题，我们集成来自DiSR-NeRF框架的迭代3D同步（I3DS）机制。在LLFF数据集上的定量与定性评估表明，本系统性能优于现有方法如DiSR-NeRF。
原文摘要 · Abstract (English)
We present a novel method for diffusion-guided frameworks for view-consistent super-resolution (SR) in neural rendering. Our approach leverages existing 2D SR models in conjunction with advanced techniques such as Variational Score Distilling (VSD) and a LoRA fine-tuning helper, with spatial training to significantly boost the quality and consistency of upscaled 2D images compared to the previous methods in the literature, such as Renoised Score Distillation (RSD) proposed in DiSR-NeRF (1), or SDS proposed in DreamFusion. The VSD score facilitates precise fine-tuning of SR models, resulting in high-quality, view-consistent images. To address the common challenge of inconsistencies among independent SR 2D images, we integrate Iterative 3D Synchronization (I3DS) from the DiSR-NeRF framework. Our quantitative benchmarks and qualitative results on the LLFF dataset demonstrate the superior performance of our system compared to existing methods such as DiSR-NeRF.
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