从3D点数据中恢复球体

C. Witzgall, G. Cheok, Anthony J. Kearsley
{"title":"从3D点数据中恢复球体","authors":"C. Witzgall, G. Cheok, Anthony J. Kearsley","doi":"10.1109/AIPR.2006.33","DOIUrl":null,"url":null,"abstract":"The National Institute of Standards and Technology is involved in developing standard protocols for the performance evaluation of 3D imaging systems, which include laser scanners and LADARs (laser detection and ranging). A LADAR is an optical device that typically yields voluminous 3D \"point clouds\" by scanning scenes. In many applications, a model of an object which is present in the scene has been specified, and the task amounts to recovering this object from scan data. Specifically, the recovery of spheres from point clouds will be addressed, aiming at estimating the location of their centers and, if not known beforehand, their radii. This information can be used, for instance, to \"register \"LADAR data to a specified coordinate frame. Two experiments recovering spheres based on best-fitting data points are reported. Sphere fitting based on orthogonal least squares is compared to a novel approach, minimizing instead the squares of range errors incurred in the direction of the scan.","PeriodicalId":375571,"journal":{"name":"35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Recovering Spheres from 3D Point Data\",\"authors\":\"C. Witzgall, G. Cheok, Anthony J. Kearsley\",\"doi\":\"10.1109/AIPR.2006.33\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The National Institute of Standards and Technology is involved in developing standard protocols for the performance evaluation of 3D imaging systems, which include laser scanners and LADARs (laser detection and ranging). A LADAR is an optical device that typically yields voluminous 3D \\\"point clouds\\\" by scanning scenes. In many applications, a model of an object which is present in the scene has been specified, and the task amounts to recovering this object from scan data. Specifically, the recovery of spheres from point clouds will be addressed, aiming at estimating the location of their centers and, if not known beforehand, their radii. This information can be used, for instance, to \\\"register \\\"LADAR data to a specified coordinate frame. Two experiments recovering spheres based on best-fitting data points are reported. Sphere fitting based on orthogonal least squares is compared to a novel approach, minimizing instead the squares of range errors incurred in the direction of the scan.\",\"PeriodicalId\":375571,\"journal\":{\"name\":\"35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-10-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AIPR.2006.33\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AIPR.2006.33","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9

摘要

美国国家标准与技术研究所参与开发3D成像系统性能评估的标准协议,其中包括激光扫描仪和LADARs(激光探测和测距)。雷达是一种光学设备,通常通过扫描场景产生大量的3D“点云”。在许多应用程序中,已经指定了场景中存在的对象的模型,并且任务相当于从扫描数据中恢复该对象。具体地说,将讨论从点云中恢复球体的问题,旨在估计其中心的位置,如果事先不知道,则估计其半径。例如,该信息可用于将LADAR数据“注册”到指定的坐标框架。报道了两个基于最佳拟合数据点的球体回收实验。将基于正交最小二乘的球面拟合方法与一种新的方法进行了比较,该方法将扫描方向上产生的距离误差的平方最小化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Recovering Spheres from 3D Point Data
The National Institute of Standards and Technology is involved in developing standard protocols for the performance evaluation of 3D imaging systems, which include laser scanners and LADARs (laser detection and ranging). A LADAR is an optical device that typically yields voluminous 3D "point clouds" by scanning scenes. In many applications, a model of an object which is present in the scene has been specified, and the task amounts to recovering this object from scan data. Specifically, the recovery of spheres from point clouds will be addressed, aiming at estimating the location of their centers and, if not known beforehand, their radii. This information can be used, for instance, to "register "LADAR data to a specified coordinate frame. Two experiments recovering spheres based on best-fitting data points are reported. Sphere fitting based on orthogonal least squares is compared to a novel approach, minimizing instead the squares of range errors incurred in the direction of the scan.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Evaluation of Algorithms for Tracking Multiple Objects in Video Rapid Automated Polygonal Image Decomposition Application Development Framework for the Rapid Integration of High Performance Image Processing Algorithms Automatic Alignment of Color Imagery onto 3D Laser Radar Data A Rate Distortion Method for Beamforming in RF Image Formation
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1