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

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. %in terms of detail preservation and distribution uniformity.

PU-Flow: a Point Cloud Upsampling Networkwith Normalizing Flows

Point cloud upsampling aims to generate dense point clouds from given sparse ones, which is a challenging task due to the irregular and unordered nature of point sets. To address this issue, we present a novel deep learning-based model, called PU-Flow,which incorporates normalizing flows and feature interpolation techniques to produce dense points uniformly distributed on the underlying surface. Specifically, we formulate the upsampling process as point interpolation in a latent space, where the interpolation weights are adaptively learned from local geometric context, and exploit the invertible characteristics of normalizing flows to transform points between Euclidean and latent spaces. We evaluate PU-Flow on a wide range of 3D models with sharp features and high-frequency details. Qualitative and quantitative results show that our method outperforms state-of-the-art deep learning-based approaches in terms of reconstruction quality, proximity-to-surface accuracy, and computation efficiency.