  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.
