检索规则说明:AND代表“并且”;OR代表“或者”;NOT代表“不包含”;(注意必须大写,运算符两边需空一格)
检 索 范 例 :范例一: (K=图书馆学 OR K=情报学) AND A=范并思 范例二:J=计算机应用与软件 AND (U=C++ OR U=Basic) NOT M=Visual
作 者:封雷 朱登明[1] 李兆歆 王兆其[1] FENG Lei;ZHU Deng-ming;LI Zhao-xin;WANG Zhao-qi(Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China;School of Computer Science and Technology,University of Chinese Academy of Sciences,Beijing 100049,China)
机构地区:[1]中国科学院计算技术研究所,北京100190 [2]中国科学院大学计算机科学与技术学院,北京100049
出 处:《计算机科学》2022年第5期25-32,共8页Computer Science
基 金:国家重点研发计划(2020YFB1710400);中国科学院科研仪器设备研制项目(YJKYYQ20190055)。
摘 要:基于图像的三维重建硬件约束小、成本低、灵活度高,在实际中得到广泛应用,但物体各部分之间存在遮挡,导致由图像生成的三维点云数据稀疏和密度不均等问题,一直是处理的难点和热点。文中提出一种基于遮罩的稀疏点云滤波算法。首先计算点云的包围盒,并在包围盒中根据点云的稀疏度自适应地划分栅格;其次,利用深度优先搜索,递归求出所有由栅格组成的自定义连通域;然后基于量化重要性指标来自适应计算阈值,通过该自适应阈值选择应保留的连通域,将所有保留的连通域集合定义为遮罩,用于描述稀疏点云的全局空间拓扑信息;最后,保留遮罩覆盖区域的点云,剔除遮罩未覆盖区域的点云,从而达到滤除离群点的目的。该方法能很好地处理由于遮挡生成的、空间疏密程度有较大差异的点云数据,可以有效去除原始三维点云数据中的离群点,同时较好地保持点云的细节信息。Image-based 3D reconstruction is widely used in practice due to less hardware constraints,lower cost and higher flexibility.Especially for the problems of sparseness and uneven density of the three-dimensional point cloud data generated by the image due to the occlusion between various parts of the object,it has always been a difficulty and hot issue to deal with.In this paper,a mask-based sparse point cloud filtering algorithm is proposed.Firstly,the bounding box of the point cloud is calculated and the grid is adaptively divided according to the sparseness of the point cloud.Secondly,Depth-first search is used to recursively find all customized connected domains composed of grids generated at the first step.Then adaptively calculating the threshold based on the quantized importance index,selecting the connected domains that should be retained based on the adaptive threshold,and defining the set of all retained connected domains as a mask,which is used to describe the global spatial topology information of the sparse point cloud.Finally,points covered by the mask are retained while points of the uncovered area are removed,so as to filter the outliers.This method can handle the point cloud data generated by occlusion and with great differences in spatial density.It can effectively remove outliers in the original three-dimensional point cloud data,while maintaining the detailed information of the point cloud.
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在链接到云南高校图书馆文献保障联盟下载...
云南高校图书馆联盟文献共享服务平台 版权所有©
您的IP:216.73.216.13