辽宁石油化工大学学报2023，Vol.43Issue(6)：89-96,8.DOI:10.12422/j.issn.1672-6952.2023.06.014
基于位置自适应的三维点云处理模型
3D Point Cloud Processing Model Based on Local Position Adaptation
摘要
Abstract
In the field of point cloud processing,deep learning is a mainstream method,but the existing methods do not fully utilize the local structure information of 3D point clouds,and have less local shape perception.We proposes a 3D point cloud processing model based on improved PoinetNet.Network model introduces position adaptive convolution into PointNet.The position-adaptive convolution constructs the kernel function by combining the weight matrices in the weight bank in a dynamic way,in which the coefficients of the weight matrix are adaptively learned from the relative positions of the points through the position-relative coefficient network.The kernel function constructed in this way can better solve the problem of irregularity and disorder of point cloud data.The classification accuracy of the position-adaptive network in the 3D object classification experiment is 3.60%higher than that of PointNet,and the average intersection ratio in the 3D object part segmentation experiment is 2.20%higher than that of PointNet.In the 3D scene semantics In the segmentation experiment,the average intersection and union ratio is improved by 9.14%compared with PointNet.关键词
点云/深度学习/局部位置自适应/分类/零件分割/语义分割Key words
Point cloud/Deep learning/Local position adaptation/Classification/Part segmentation/Scene segmentation分类
信息技术与安全科学引用本文复制引用
侯健,刘恒,刘琳珂,潘斌,张玉萍..基于位置自适应的三维点云处理模型[J].辽宁石油化工大学学报,2023,43(6):89-96,8.基金项目
国家自然科学基金资助项目(61602228,61572290) （61602228,61572290）
辽宁省教育厅一般项目(L2020018) （L2020018）
辽宁省"兴辽英才计划"青年拔尖人才项目(XLYC1807266) （XLYC1807266）
辽宁省自然科学基金项目(201502041) （201502041）
山东省自然科学基金项目(ZR2018MF006). （ZR2018MF006）
