DiSR-NeRF: Diffusion-Guided View-Consistent Super-Resolution of Neural Radiance Fields

Core Concepts

DiSR-NeRF leverages diffusion models to generate high-quality, view-consistent details for super-resolution of neural radiance fields from low-resolution input images.

Abstract

The paper presents DiSR-NeRF, a method for achieving high-quality super-resolution of neural radiance fields (NeRFs) using only low-resolution (LR) input images. The key contributions are:

Iterative 3D Synchronization (I3DS): This two-stage process alternates between upscaling LR NeRF renders using a diffusion-based 2D super-resolution model, and then synchronizing the details into the 3D NeRF representation through standard NeRF training. This helps resolve cross-view inconsistencies.

Renoised Score Distillation (RSD): RSD is a novel score-distillation objective that combines the strengths of ancestral sampling (generating sharp details) and Score Distillation Sampling (maintaining consistency with the LR conditioning). RSD optimizes the intermediate denoised latents of the ancestral sampling trajectory, producing sharper details that are also LR-consistent.

The authors show that DiSR-NeRF can outperform existing baselines on both synthetic and real-world datasets, generating high-resolution NeRFs with view-consistent details from only LR input images, without requiring high-resolution reference data.

Stats

"Imaging devices may be limited in resolution (i.e., drones, CCTVs, etc.) and consequently high-resolution multi-view images may be unavailable."
"Collecting such large-scale, high-resolution multi-view data is labor-intensive and requires expensive equipment to obtain accurate scans."

Quotes

"We thus propose to leverage knowledge from the 2D super-resolution models to circumvent the requirements for HR images."
"Naively upscaling individual LR training images with 2D super-resolution methods produces SR images that may not be consistent across views."
"The alternating process between the two stages guides the NeRF to converge to view-consistent details."
"RSD is able to achieve sharper details compared to SDS while also producing LR-consistent features compared to ancestral sampling."

Key Insights Distilled From

DiSR-NeRF

by Jie Long Lee... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00874.pdf

Deeper Inquiries

How can DiSR-NeRF be extended to handle even lower resolution input images or handle more challenging real-world scenes

DiSR-NeRF can be extended to handle even lower resolution input images or more challenging real-world scenes by incorporating advanced techniques in the upscaling and synchronization stages. For handling lower resolution input images, the upscaling process can be enhanced by utilizing more powerful 2D super-resolution models or cascaded diffusion models to achieve higher super-resolution factors. Additionally, the synchronization stage can be optimized to better capture view-consistent details by incorporating more sophisticated optimization algorithms or leveraging multi-scale features for better convergence. Moreover, the training process can be augmented with data augmentation techniques to improve the model's robustness to handle more challenging real-world scenes with varying complexities and lighting conditions.

What are the potential limitations of the diffusion-based upscaling approach, and how could they be addressed in future work

One potential limitation of the diffusion-based upscaling approach in DiSR-NeRF is the restricted upscaling factor due to the predefined capabilities of the Stable Diffusion ×4 Upscaler. To address this limitation in future work, researchers can explore the use of cascaded diffusion models or hierarchical diffusion processes to achieve higher super-resolution factors. By incorporating multiple stages of diffusion-based upscaling, the model can effectively handle lower resolution input images and produce more detailed and high-resolution outputs. Additionally, researchers can investigate the integration of self-supervised learning techniques or adversarial training to enhance the quality and consistency of the upscaling process in challenging scenarios.

How could the insights from DiSR-NeRF be applied to other 3D reconstruction tasks beyond just neural radiance fields

The insights from DiSR-NeRF can be applied to other 3D reconstruction tasks beyond neural radiance fields by adapting the diffusion-guided framework to different types of 3D representations. For tasks such as point cloud reconstruction or mesh generation, the principles of leveraging 2D super-resolution models, iterative synchronization, and score distillation can be applied to enhance the quality and consistency of the reconstructed 3D structures. By incorporating diffusion-based techniques into various 3D reconstruction tasks, researchers can improve the fidelity, detail, and view-consistency of the generated 3D models. Additionally, the concepts of iterative refinement and multi-view consistency can be beneficial for tasks requiring accurate and high-resolution 3D reconstructions in diverse applications such as robotics, augmented reality, and virtual reality.