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Advancing Super-Resolution in Neural Radiance Fields via Variational Diffusion Strategies

Shrey Vishen, Jatin Sarabu, Saurav Kumar, Chinmay Bharathulwar, Rithwick Lakshmanan, Vishnu Srinivas

TL;DR

This work tackles the challenge of achieving high-resolution, view-consistent rendering in Neural Radiance Fields by introducing Variational Score Distillation (VSD) combined with LoRA fine-tuning and Iterative 3D Synchronization (I3DS). The method leverages diffusion-based latent refinement, a dual UNet setup (frozen and fine-tuned with LoRA), and a LR NeRF foundation to produce superior SR results on LLFF benchmarks, outperforming prior approaches such as SDS and RSD in key metrics like NIQE and PSNR. An extensive ablation study confirms the contributions of LoRA and I3DS, while acknowledging trade-offs in perceptual similarity (LPIPS) and additional computational cost. The proposed framework advances NeRF-based SR by delivering more photorealistic, detailed, and consistent 3D reconstructions, with implications for interactive rendering, visualization, and graphics-heavy applications.

Abstract

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.

Advancing Super-Resolution in Neural Radiance Fields via Variational Diffusion Strategies

TL;DR

This work tackles the challenge of achieving high-resolution, view-consistent rendering in Neural Radiance Fields by introducing Variational Score Distillation (VSD) combined with LoRA fine-tuning and Iterative 3D Synchronization (I3DS). The method leverages diffusion-based latent refinement, a dual UNet setup (frozen and fine-tuned with LoRA), and a LR NeRF foundation to produce superior SR results on LLFF benchmarks, outperforming prior approaches such as SDS and RSD in key metrics like NIQE and PSNR. An extensive ablation study confirms the contributions of LoRA and I3DS, while acknowledging trade-offs in perceptual similarity (LPIPS) and additional computational cost. The proposed framework advances NeRF-based SR by delivering more photorealistic, detailed, and consistent 3D reconstructions, with implications for interactive rendering, visualization, and graphics-heavy applications.

Abstract

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.

Paper Structure

This paper contains 15 sections, 10 equations, 6 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Methodolgy
  3. Pre-Requisites
  4. Latent Encoding and Residual Learning
  5. Forward Diffusion Process
  6. UNet-Based Prediction
  7. Low-rank Adaptation (LoRA) Fine-Tuning
  8. Lower Resolution NeRF Generation
  9. SR Training Process
  10. Iterative 3D Sync Process
  11. Experimental Results
  12. Statistical Analysis
  13. Comparison with Existing Methods
  14. Limitations
  15. Conclusion

Figures (6)

  • Figure 1: NeRF diagram
  • Figure 2: Refer to Algorithm 1
  • Figure 3: Refer to Algorithm 2
  • Figure 4: Comparison of RSD and VSD on Museum Artifact
  • Figure 5: Comparison of SDS and VSD on Flower Images
  • ...and 1 more figures