Out-of-core outlier removal for large-scale indoor point clouds

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Graphical Models Pub Date : 2022-07-01 DOI:10.1016/j.gmod.2022.101142

Linlin Ge, Jieqing Feng

引用次数: 0

Abstract

An accurate coarse-to-fine out-of-core outlier removal method is proposed for large-scale indoor point clouds by mining the geometric shape constraints. In coarse processing stage, a low-resolution point cloud (LPC) is obtained using random downsampling. LPC has the same density distribution as the raw point clouds (RPC), which is important information for outlier removal. The correspondences from the LPC to the RPC are also recorded. The outliers in the LPC are removed via a global threshold. The outliers in the RPC are roughly removed guided by the cleaned LPC. In refinement processing stage, the cleaned LPC is segmented into planar and non-planar segments; and the LPC segmentation is transferred to the RPC. Finally, the outliers in each RPC segment are removed elaborately via a local threshold by exploring the shape information. The experiments show that the proposed method improves the quality of outlier removal results.

Abstract Image

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

大规模室内点云的核外离群值去除

通过挖掘几何形状约束，提出了一种针对室内大尺度点云的精确粗到精的核心外离群点去除方法。在粗处理阶段，采用随机下采样获得低分辨率点云。LPC具有与原始点云(RPC)相同的密度分布，这是去除离群值的重要信息。从LPC到RPC的通信也被记录下来。LPC中的异常值通过全局阈值去除。在清理后的LPC的引导下，RPC中的异常值被粗略地移除。在精加工阶段，将清洗后的LPC分割为平面段和非平面段;将LPC分段传送给RPC。最后，通过探索形状信息，通过局部阈值精细地去除每个RPC段中的异常值。实验表明，该方法提高了异常值去除结果的质量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Graphical Models 工程技术-计算机：软件工程

CiteScore

3.60

自引率

5.90%

发文量

审稿时长

47 days

期刊介绍： Graphical Models is recognized internationally as a highly rated, top tier journal and is focused on the creation, geometric processing, animation, and visualization of graphical models and on their applications in engineering, science, culture, and entertainment. GMOD provides its readers with thoroughly reviewed and carefully selected papers that disseminate exciting innovations, that teach rigorous theoretical foundations, that propose robust and efficient solutions, or that describe ambitious systems or applications in a variety of topics. We invite papers in five categories: research (contributions of novel theoretical or practical approaches or solutions), survey (opinionated views of the state-of-the-art and challenges in a specific topic), system (the architecture and implementation details of an innovative architecture for a complete system that supports model/animation design, acquisition, analysis, visualization?), application (description of a novel application of know techniques and evaluation of its impact), or lecture (an elegant and inspiring perspective on previously published results that clarifies them and teaches them in a new way). GMOD offers its authors an accelerated review, feedback from experts in the field, immediate online publication of accepted papers, no restriction on color and length (when justified by the content) in the online version, and a broad promotion of published papers. A prestigious group of editors selected from among the premier international researchers in their fields oversees the review process.