摘要
针对点云分割中边缘特征提取不足导致局部特征信息不完整、整体分割精度下降的问题,提出一种3D点云分割改进型边缘特征提取网络。为提升边缘特征表达能力,对深层边缘特征提取层做出改进,引入基于残差结构改进的多层感知机结构,形成边缘特征提取单元,该单元将原始点云特征与多层感知机提取边缘特征相融合,获取更加丰富完整的边缘信息,提高3D点云分割网络模型精度。在Shapenet数据集上的实验结果表明,提出的3D点云分割改进型边缘特征提取网络优于现存同类方法,相较于LDGCNN链接动态图网络,点云分割准确率提升了1.17%。
An improved edge extraction network based on 3D point cloud segmentation was proposed to solve the problem of incomplete local feature information and decreased overall segmentation accuracy caused by insufficient edge feature extraction in point cloud segmentation.To enhance edge feature expression ability and improve the deep edge feature extract layer,we introduced a multilayer perceptron structure based on the residual structure improvement,and formed edge feature extraction units.The units can integrate the original point cloud features with the edge features extracted by the multi-layer perceptron to obtain edge information with more richness and integrity and improve the precision of 3D point cloud segmentation network.The experimental results on Shapenet data set show that the proposed improved edge extraction network for 3D point cloud segmentation is superior to the existing similar methods.The accuracy of point cloud segmentation is improved by 1.17%,compared with LDGCNN.
作者
向姝芬
毛琳
杨大伟
XIANG SHU-fen;MAO Lin;YANG Da-wei(School of Electromechanical Engineering, Dalian Minzu University, Dalian Liaoning 116605, China)
出处
《大连民族大学学报》
2021年第5期417-421,共5页
Journal of Dalian Minzu University
基金
国家自然科学基金项目(61673084)
辽宁省自然科学基金项目(20170540192,20180550866)。
关键词
点云分割
多层感知机
边缘特征
残差结构
point cloud segmentation
multilayer perceptron
edge feature
residual structure
作者简介
向姝芬(1998-),女,土家族,湖北恩施人,大连民族大学机电工程学院硕士研究生,主要从事计算机视觉3D点云研究;通讯作者:毛琳(1977-),女,吉林省吉林市人,副教授,博士,主要从事多传感器信息融合、目标跟踪研究,E-mail:maolin@dlnu.edu.cn。
