MSPoint:基于多尺度分布分数的点云去噪网络

MSPoint:Point Cloud Denoising Network Based on Multiscale Distribution Score

激光扫描仪等设备直接收集到的原始点云通常会受到噪声的影响,这会影响后续的处理,如三维重建、语义分割等,因此点云去噪算法尤为重要。现有的点云去噪网络大多以噪声点与干净点的距离作为目标函数进行迭代训练,这可能导致点云聚集与异常值。针对以上问题,提出一种基于多尺度点云分布分数(即点云对数概率函数的梯度)的新型去噪网络multiscale score point(MSPoint)。MSPoint网络主要由两部分组成:特征提取模块和位移预测模块。在特征提取模块中输入点云的邻域,通过对数据添加多尺度噪声扰动加强MSPoint的抗噪性能,使提取到的特征具有更强的表达能力。位移预测模块根据分数估计单元预测的分数迭代学习噪声点的位移。在公开数据集上的实验结果表明,相比现有的方法,MSPoint有着更好的去噪效果以及更强的鲁棒性。

The original point cloud obtained directly by equipment such as laser scanners is usually affected by noise,which will affect subsequent processing,such as three-dimensional reconstruction and semantic segmentation;as a result,the point cloud denoising algorithm becomes particularly crucial.The majority of currently available point cloud denoising networks use the distance between noise and clean points as the objective function during iterative training,which may cause point cloud aggravation and outliers.To address the above issues,a new denoising network called multiscale score point(MSPoint)based on multiscale point cloud distribution fraction(i.e.,the gradient of point-cloud logarithmic probability function)is proposed.The displacement prediction and feature extraction modules make up the majority of the MSPoint network.Input the neighborhood of the point cloud in the feature extraction module and strengthen the antinoise performance of MSPoint by adding multiscale noise disturbance to the data,thereby leading the extracted features to have a stronger expression ability.According to the fraction predicted by the fraction estimation unit,the displacement prediction module iteratively learns the displacement of noise points.MSPoint provides stronger robustness than previous approaches and a superior denoising impact,according to experimental results on public datasets.