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

TL;DR

PD-Flow introduces a novel point cloud denoising framework using normalizing flows and distribution learning, effectively disentangling noise from clean data to improve accuracy on synthetic and real datasets.

Contribution

The paper proposes a new deep learning model that formulates point cloud denoising as a distribution learning problem using normalizing flows and noise disentanglement techniques.

Findings

Outperforms previous state-of-the-art methods in denoising accuracy.

Effective on both synthetic and real-world noisy point clouds.

Demonstrates superior qualitative and quantitative results.

Abstract

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.