Largely due to their implicit nature, neural fields lack a direct mechanism
for filtering, as Fourier analysis from discrete signal processing is not
directly applicable to these representations. Effective filtering of neural
fields is critical to enable level-of-detail processing in downstream
applications, and support operations that involve sampling the field on regular
grids (e.g. marching cubes). Existing methods that attempt to decompose neural
fields in the frequency domain either resort to heuristics or require extensive
modifications to the neural field architecture. We show that via a simple
modification, one can obtain neural fields that are low-pass filtered, and in
turn show how this can be exploited to obtain a frequency decomposition of the
entire signal. We demonstrate the validity of our technique by investigating
level-of-detail reconstruction, and showing how coarser representations can be
computed effectively.

通过简单修改，我们可以获得经过低通滤波的神经场，并展示如何利用此技术获得整个信号的频率分解，从而有效地计算出更粗糙的表示形式。

BANF：用于详细层次重建的带限神经场模型

BANF: Band-limited Neural Fields for Levels of Detail Reconstruction

This paper enables high-fidelity, transferable NeRF editing by frequency
decomposition. Recent NeRF editing pipelines lift 2D stylization results to 3D
scenes while suffering from blurry results, and fail to capture detailed
structures caused by the inconsistency between 2D editings. Our critical
insight is that low-frequency components of images are more
multiview-consistent after editing compared with their high-frequency parts.
Moreover, the appearance style is mainly exhibited on the low-frequency
components, and the content details especially reside in high-frequency parts.
This motivates us to perform editing on low-frequency components, which results
in high-fidelity edited scenes. In addition, the editing is performed in the
low-frequency feature space, enabling stable intensity control and novel scene
transfer. Comprehensive experiments conducted on photorealistic datasets
demonstrate the superior performance of high-fidelity and transferable NeRF
editing. The project page is at https://aigc3d.github.io/freditor.

通过频率分解实现高保真、可迁移的 NeRF 编辑。

Freditor：基于频率分解的高保真可迁移 NeRF 编辑

Freditor: High-Fidelity and Transferable NeRF Editing by Frequency  Decomposition

Traditional single image super-resolution (SISR) methods that focus on
solving single and uniform degradation (i.e., bicubic down-sampling), typically
suffer from poor performance when applied into real-world low-resolution (LR)
images due to the complicated realistic degradations. The key to solving this
more challenging real image super-resolution (RealSR) problem lies in learning
feature representations that are both informative and content-aware. In this
paper, we propose an Omni-frequency Region-adaptive Network (ORNet) to address
both challenges, here we call features of all low, middle and high frequencies
omni-frequency features. Specifically, we start from the frequency perspective
and design a Frequency Decomposition (FD) module to separate different
frequency components to comprehensively compensate the information lost for
real LR image. Then, considering the different regions of real LR image have
different frequency information lost, we further design a Region-adaptive
Frequency Aggregation (RFA) module by leveraging dynamic convolution and
spatial attention to adaptively restore frequency components for different
regions. The extensive experiments endorse the effective, and scenario-agnostic
nature of our OR-Net for RealSR.

通过使用频率分解和区域自适应频率聚合，我们设计了一种全频率区域自适应网络（ORNet），用于实现真实图像的超分辨率重建，该网络的训练基于特征表示，能够补偿真实低分辨率图像中的信息丢失，并具有场景不可知性。