学习几何结构特征的真实点云场景语义分割

Semantic Segmentation for Real Point Cloud Scenes via Geometric Features

有效获取点云数据在空间上的结构性特征是点云语义分割的关键。针对以往方法没有很好综合利用全局和局部特征问题,提出一种新的空间结构特征——点的盒子特征用于语义分割,设计一种编码‑解码结构的网络框架,下采样过程中使用几何结构特征模块学习点云的全局空间特征和局部邻域特征,上采样过程中按分辨率逐级恢复成完整尺寸特征图进行语义分割。其中,几何结构特征模块包含两个子模块,一个是全局特征模块,该模块学习点的“盒子(box)”特征以表现点云在采样空间内概括的粗糙几何特征;另一个是局部特征模块,该模块使用特征提取——注意力机制结构表现点云在局部邻域内精确的细粒度几何特征。在公开数据集S3DIS、Semantic3D上进行了实验并与其他方法比较,实验结果表明mIoU均领先目前大部分主流的方法,部分细则类IoU取得最高。

Effective acquisition of spatial structural features of point cloud data is the key to semantic segmentation of point clouds.To solve the problem that the previous methods do not make good use of global and local features,a new spatial structure feature,point box feature,is proposed for semantic segmentation.A network framework of encoding-decoding structure is designed.The global spatial and local neighborhood features of point clouds are learned by using the geometric structure feature module during the downsampling process,and the full size feature map is restored step by step in the upper sampling process for semantic segmentation.The geometric structure features module contains two sub-modules,one is the global features module,which learns the“box”features of points to represent the rough geometric features of point clouds in the sampling space.Another is the local features module,which uses feature extraction,the attention mechanism structure,to represent precise,fine-grained geometric characteristics of point clouds within local neighborhoods.Experiments are performed on the public dataset S3DIS and Semantic3D and compared with other methods.The results show that mIoU is ahead of most of the current mainstream methods,and some of the detail class IoU is the highest.