We present DiSR-NeRF, a diffusion-guided framework for view-consistent
super-resolution (SR) NeRF. Unlike prior works, we circumvent the requirement
for high-resolution (HR) reference images by leveraging existing powerful 2D
super-resolution models. Nonetheless, independent SR 2D images are often
inconsistent across different views. We thus propose Iterative 3D
Synchronization (I3DS) to mitigate the inconsistency problem via the inherent
multi-view consistency property of NeRF. Specifically, our I3DS alternates
between upscaling low-resolution (LR) rendered images with diffusion models,
and updating the underlying 3D representation with standard NeRF training. We
further introduce Renoised Score Distillation (RSD), a novel score-distillation
objective for 2D image resolution. Our RSD combines features from ancestral
sampling and Score Distillation Sampling (SDS) to generate sharp images that
are also LR-consistent. Qualitative and quantitative results on both synthetic
and real-world datasets demonstrate that our DiSR-NeRF can achieve better
results on NeRF super-resolution compared with existing works. Code and video
results available at the project website.

DiSR-NeRF 是一个利用扩散引导的框架来实现视图一致的超分辨率 NeRF，该方法通过利用现有的强大 2D 超分辨率模型来规避高分辨率参考图像的要求，并通过 NeRF 的内在多视图一致性属性提出了迭代的 3D 同步技术来减轻 LR 渲染图像之间不一致的问题。通过 Renoised Score Distillation，我们进一步引入了一种新颖的用于 2D 图像分辨率的评分蒸馏目标函数。我们的 DiSR-NeRF 在合成和真实世界数据集上进行的定性和定量结果表明，与现有方法相比，我们的方法在 NeRF 超分辨率上可以取得更好的效果。

DiSR-NeRF：扩散引导的视角一致超分辨率 NeRF

DiSR-NeRF: Diffusion-Guided View-Consistent Super-Resolution NeRF

We present a method to efficiently generate 3D-aware high-resolution images
that are view-consistent across multiple target views. The proposed multiplane
neural radiance model, named GMNR, consists of a novel {\alpha}-guided
view-dependent representation ({\alpha}-VdR) module for learning view-dependent
information. The {\alpha}-VdR module, faciliated by an {\alpha}-guided pixel
sampling technique, computes the view-dependent representation efficiently by
learning viewing direction and position coefficients. Moreover, we propose a
view-consistency loss to enforce photometric similarity across multiple views.
The GMNR model can generate 3D-aware high-resolution images that are
viewconsistent across multiple camera poses, while maintaining the
computational efficiency in terms of both training and inference time.
Experiments on three datasets demonstrate the effectiveness of the proposed
modules, leading to favorable results in terms of both generation quality and
inference time, compared to existing approaches. Our GMNR model generates
3D-aware images of 1024 X 1024 pixels with 17.6 FPS on a single V100. Code :
this https URL

该研究提出了一种名为 GMNR 的多平面神经辐亮度模型，其包含一个用于学习视角相关信息的新型 α 引导视角依赖表示模块（α-VdR 模块）和一个视角一致性损失，可以高效地生成视角一致的 3D 感知高分辨率图像。

多层面生成神经辐射用于三维感知图像生成

Generative Multiplane Neural Radiance for 3D-Aware Image Generation

We have witnessed rapid progress on 3D-aware image synthesis, leveraging
recent advances in generative visual models and neural rendering. Existing
approaches however fall short in two ways: first, they may lack an underlying
3D representation or rely on view-inconsistent rendering, hence synthesizing
images that are not multi-view consistent; second, they often depend upon
representation network architectures that are not expressive enough, and their
results thus lack in image quality. We propose a novel generative model, named
Periodic Implicit Generative Adversarial Networks ($\pi$-GAN or pi-GAN), for
high-quality 3D-aware image synthesis. $\pi$-GAN leverages neural
representations with periodic activation functions and volumetric rendering to
represent scenes as view-consistent 3D representations with fine detail. The
proposed approach obtains state-of-the-art results for 3D-aware image synthesis
with multiple real and synthetic datasets.

使用周期性激活函数和体积渲染的神经表示，提出了一种新的生成模型，命名为周期性隐式生成对抗网络，用于高质量的 3D 感知图像合成，其能够表示多视角一致的 3D 场景，并在多个真实和合成数据集上获得了最先进的结果。