基于空间感知和特征增强的三维点云分类与分割研究

针对直接处理点云数据的深度神经网络PointNet++无法充分学习点云形状信息的问题,提出一种融合空间感知模块和特征增强模块(spatial awareness and feature enhancement,SAFE) 的三维点云分类与分割方法(SAFE-PointNet++) 。首先,设计了空间感知(spatial awareness,SA) 模块,使特征提取网络在特征升维时融合了包含空间结构的权重信息,增强了特征在空间上的表现力。其次,设计了特征增强(feature enhancement,FE) 模块,通过把增强后的几何信息和附加信息拆分并分别进行编码,达到充分利用点云附加信息的目的。实验结果表明,在ModelNet40和S3DIS数据集上,SAFE-PointNet++与其他10种经典网络相比具有更高的分类和分割精度。

Research on classification and segmentation of 3D point cloud based on spatial awareness and feature enhancement

To solve the problem that PointNet++,a direct point cloud data processing deep neural network,cannot thoroughly learn the shape information of point cloud,and SAFE-PointNet++(spatial awareness and feature enhancement PointNet++) ,a 3D point cloud classification and segmentation method is proposed,which combines both spatial awareness module and feature enhancement module (SAFE).Firstly,the spatial awareness (SA) module is designed to help the feature extraction network integrate the weight information of spatial structure when the feature dimension is raised,thus enhancing the expression function of the feature in space.Secondly,the feature enhancement (FE) module is designed so that the additional information of the point cloud can be fully used by respectively splitting and encoding the enhanced geometric information and additional information.The experiment results show that SAFE-PointNet++ achieves higher classification and segmentation accuracy than the other ten classical networks on ModelNet40 and S3DIS datasets.