Point cloud filtering is a fundamental 3D vision task, which aims to remove
noise while recovering the underlying clean surfaces. State-of-the-art methods
remove noise by moving noisy points along stochastic trajectories to the clean
surfaces. These methods often require regularization within the training
objective and/or during post-processing, to ensure fidelity. In this paper, we
introduce StraightPCF, a new deep learning based method for point cloud
filtering. It works by moving noisy points along straight paths, thus reducing
discretization errors while ensuring faster convergence to the clean surfaces.
We model noisy patches as intermediate states between high noise patch variants
and their clean counterparts, and design the VelocityModule to infer a constant
flow velocity from the former to the latter. This constant flow leads to
straight filtering trajectories. In addition, we introduce a DistanceModule
that scales the straight trajectory using an estimated distance scalar to
attain convergence near the clean surface. Our network is lightweight and only
has $\sim530K$ parameters, being 17% of IterativePFN (a most recent point cloud
filtering network). Extensive experiments on both synthetic and real-world data
show our method achieves state-of-the-art results. Our method also demonstrates
nice distributions of filtered points without the need for regularization. The
implementation code can be found at: this https URL

点云过滤是一项基础的三维视觉任务，本文提出了一种基于深度学习的新方法 StraightPCF，通过沿直线路径移动噪点以减少离散化误差和更快地收敛到干净表面，该方法在合成和实际数据上均取得了最先进的结果。

StraightPCF: 直接点云滤波

StraightPCF: Straight Point Cloud Filtering

Point cloud filtering, the main bottleneck of which is removing noise
(outliers) while preserving geometric features, is a fundamental problem in 3D
field. The two-step schemes involving normal estimation and position update
have been shown to produce promising results. Nevertheless, the current normal
estimation methods including optimization ones and deep learning ones, often
either have limited automation or cannot preserve sharp features. In this
paper, we propose a novel feature-preserving normal estimation method for point
cloud filtering with preserving geometric features. It is a learning method and
thus achieves automatic prediction for normals. For training phase, we first
generate patch based samples which are then fed to a classification network to
classify feature and non-feature points. We finally train the samples of
feature and non-feature points separately, to achieve decent results. Regarding
testing, given a noisy point cloud, its normals can be automatically estimated.
For further point cloud filtering, we iterate the above normal estimation and a
current position update algorithm for a few times. Various experiments
demonstrate that our method outperforms state-of-the-art normal estimation
methods and point cloud filtering techniques, in terms of both quality and
quantity.

该研究提出了一种新的基于特征保留的法线估计方法来过滤点云，并通过实验证明其在去除噪点和保留几何特征方面的效果优于现有法线估计方法和点云过滤技术。

深度特征保持点云滤波法中的法向估计

Deep Feature-preserving Normal Estimation for Point Cloud Filtering

Point cloud filtering is a fundamental problem in geometry modeling and
processing. Despite of significant advancement in recent years, the existing
methods still suffer from two issues: 1) they are either designed without
preserving sharp features or less robust in feature preservation; and 2) they
usually have many parameters and require tedious parameter tuning. In this
paper, we propose a novel deep learning approach that automatically and
robustly filters point clouds by removing noise and preserving their sharp
features. Our point-wise learning architecture consists of an encoder and a
decoder. The encoder directly takes points (a point and its neighbors) as
input, and learns a latent representation vector which goes through the decoder
to relate the ground-truth position with a displacement vector. The trained
neural network can automatically generate a set of clean points from a noisy
input. Extensive experiments show that our approach outperforms the
state-of-the-art deep learning techniques in terms of both visual quality and
quantitative error metrics. The source code and dataset can be found at
this https URL

本文提出了一种新的深度学习方法，通过去除噪声并保持尖锐特征，自动而又稳健地过滤点云，其点对点的学习架构由编码器和解码器组成，并在视觉质量和数量误差指标方面均优于现有深度学习技术。