Point cloud denoising aims to restore clean point clouds from raw
observations corrupted by noise and outliers while preserving the fine-grained
details. We present a novel deep learning-based denoising model, that
incorporates normalizing flows and noise disentanglement techniques to achieve
high denoising accuracy. Unlike existing works that extract features of point
clouds for point-wise correction, we formulate the denoising process from the
perspective of distribution learning and feature disentanglement. By
considering noisy point clouds as a joint distribution of clean points and
noise, the denoised results can be derived from disentangling the noise
counterpart from latent point representation, and the mapping between Euclidean
and latent spaces is modeled by normalizing flows. We evaluate our method on
synthesized 3D models and real-world datasets with various noise settings.
Qualitative and quantitative results show that our method outperforms previous
state-of-the-art deep learning-based approaches.

介绍一种基于深度学习、归一化流以及噪音解缠技术的点云去噪方法，通过对噪声进行解缠并建立欧几里得空间和潜在空间之间的映射，从而实现高精度的去噪。在合成和真实世界数据集上的定性和定量实验表明，其性能优于现有的基于深度学习的方法。

PD-Flow：一种带有归一化流的点云去噪框架

PD-Flow: A Point Cloud Denoising Framework with Normalizing Flows

Deep learning-based methods for low-light image enhancement typically require
enormous paired training data, which are impractical to capture in real-world
scenarios. Recently, unsupervised approaches have been explored to eliminate
the reliance on paired training data. However, they perform erratically in
diverse real-world scenarios due to the absence of priors. To address this
issue, we propose an unsupervised low-light image enhancement method based on
an effective prior termed histogram equalization prior (HEP). Our work is
inspired by the interesting observation that the feature maps of histogram
equalization enhanced image and the ground truth are similar. Specifically, we
formulate the HEP to provide abundant texture and luminance information.
Embedded into a Light Up Module (LUM), it helps to decompose the low-light
images into illumination and reflectance maps, and the reflectance maps can be
regarded as restored images. However, the derivation based on Retinex theory
reveals that the reflectance maps are contaminated by noise. We introduce a
Noise Disentanglement Module (NDM) to disentangle the noise and content in the
reflectance maps with the reliable aid of unpaired clean images. Guided by the
histogram equalization prior and noise disentanglement, our method can recover
finer details and is more capable to suppress noise in real-world low-light
scenarios. Extensive experiments demonstrate that our method performs favorably
against the state-of-the-art unsupervised low-light enhancement algorithms and
even matches the state-of-the-art supervised algorithms.

提出一种无监督低光图像增强方法，基于直方图均衡化先验和噪声分离模块，能够恢复更好的细节并在真实低光情景中更有效地抑制噪声，实验证明此方法可以与最先进的监督算法相媲美。