密度先验引导的无监督深度点云降噪算法

Density Prior-Guided Unsupervised Deep Point Cloud Denoising Algorithm

为了提高无监督深度点云降噪算法的性能,提出密度先验引导的无监督深度点云降噪算法.基于现有的网络框架,首先设计了密度先验,通过噪声点云的整体分布计算每点位于真实底层曲面的概率;然后利用深度网络,通过上采、下采等策略克服大噪声点的影响,得到降噪点云;最后利用密度先验优化干净点的条件概率分布,设计无监督损失函数对网络进行优化,得到最终算法.此外,基于密度先验还提出低噪声点筛选方法和滤波算法.所提算法在PyTorch上实现,以基于ModelNet-40构造的仿真数据库及真实扫描数据PERL为例,以倒角距离及点到曲面的距离为评价指标.与DMR等算法相比,倒角距离平均降低0.35~1.34,点到曲面的距离平均降低0.68~1.94.实验结果表明,所提算法降噪结果优于现有算法,且具有较强的鲁棒性、普适性和泛化能力.

In order to improve the performance of the unsupervised deep point cloud denoising algorithm,based on the existing network framework,firstly,the density prior is designed,which uses the distribution of the noisy point clouds to describe the probability of each point being on the real underlying surface.Then,the denoised point cloud is obtained by the deep network which uses upsampling,downsampling and other strategies to overcome the influence of high-noise points.Finally,the density prior is used to optimize the conditional probability distribution of clean points,and the unsupervised loss function is designed which optimizes the network to obtain the final algorithm.In addition,based on the density prior,a low-noise points screening method and filtering algorithm are proposed.The algorithm is implemented on PyTorch,testing on the real scan dataset PERL and the simulation dataset constructed based on ModelNet-40,and taking the Chamfer distance and the point-to-surface distance as evaluation metrics.Compared with other algorithms such as DMR,the Chamfer distance is reduced by 0.35~1.34,and the point-to-surface distance is reduced by 0.68~1.94 on average.The experimental results show that the proposed algorithm outperforms state-of-the-art denoising methods with robustness,universality,and generalization ability.