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检索条件"主题词=Iterative Closest Point algorithm"
1671 条 记 录,以下是41-50 订阅
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SCM: Spatially Coherent Matching With Gaussian Field Learning for Nonrigid point Set Registration
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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021年 第1期32卷 203-213页
作者: Wang, Gang Chen, Yufei Shanghai Univ Finance & Econ Inst Data Sci & Stat Sch Stat & Management Shanghai 200433 Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China
While point set registration has been studied in many areas of computer vision for decades, registering points encountering different degradations remains a challenging problem. In this article, we introduce a robust ... 详细信息
来源: 评论
Inexact Bayesian point pattern matching for linear transformations
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PATTERN RECOGNITION 2014年 第10期47卷 3265-3275页
作者: Christmas, J. Everson, R. M. Bell, J. Winlove, C. P. Univ Exeter Dept Comp Sci Exeter EX4 4QF Devon England Univ Exeter Peninsula Coll Med & Dent Exeter EX1 2LU Devon England Univ Exeter Dept Biophys Exeter EX4 4QF Devon England
We introduce a novel Bayesian inexact point pattern matching model that assumes that a linear transformation relates the two sets of points. The matching problem is inexact due to the lack of one-toone correspondence ... 详细信息
来源: 评论
Fast and Robust iterative closest point
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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年 第7期44卷 3450-3466页
作者: Zhang, Juyong Yao, Yuxin Deng, Bailin Univ Sci & Technol China Sch Math Sci Hefei 230052 Anhui Peoples R China Cardiff Univ Sch Comp Sci & Informat Cardiff CF10 3AT Wales
The iterative closest point (ICP) algorithm and its variants are a fundamental technique for rigid registration between two point sets, with wide applications in different areas from robotics to 3D reconstruction. The... 详细信息
来源: 评论
Multilevel Optimization for Registration of Deformable point Clouds
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IEEE TRANSACTIONS ON IMAGE PROCESSING 2020年 29卷 8735-8746页
作者: Chaudhury, Ayan INRIA Grenoble Rhone Alpes F-69364 Lyon France Ecole Normale Super Lyon F-69364 Lyon France
Handling deformation is one of the biggest challenges associated with point cloud registration. When deformation happens due to the motion of an animated object which actively changes its location and general shape, r... 详细信息
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Comparison between point cloud processing techniques
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MEASUREMENT 2018年 127卷 221-226页
作者: Catalucci, Sofia Marsili, Roberto Moretti, Michele Rossi, Gianluca Univ Perugia Dipartimento Ingn Via Goffredo Duranti 93 I-06125 Perugia Italy Univ Nottingham Adv Mfg BldgJubilee Campus Nottingham NG8 1BB England
Photomodelling is an innovative, economical and fast technique based on the same principles of photogrammetry, which leads to the creation of 3-dimensional models starting from the simple acquisition of photographs. T... 详细信息
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Multientity Registration of point Clouds for Dynamic Objects on Complex Floating Platform Using Object Silhouettes
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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2021年 第1期59卷 769-783页
作者: Wang, Feng Hu, Han Ge, Xuming Xu, Bo Zhong, Ruofei Ding, Yulin Xie, Xiao Zhu, Qing Southwest Jiaotong Univ Fac Geosci & Environm Engn Chengdu 611756 Peoples R China Capital Normal Univ Beijing Adv Innovat Ctr Imaging Technol Beijing 100048 Peoples R China Zhejiang Hitarget Space Informat Technol Corp Ltd Huzhou 313200 Peoples R China
This article is focused on a challenging topic emerging from the registration of point clouds, specifically the registration of dynamic objects with low overlapping ratio. This problem is especially difficult when the... 详细信息
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Registration with the point Cloud Library A Modular Framework for Aligning in 3-D
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IEEE ROBOTICS & AUTOMATION MAGAZINE 2015年 第4期22卷 110-124页
作者: Holz, Dirk Ichim, Alexandru-Eugen Tombari, Federico Rusu, Radu B. Behnke, Sven Univ Bonn Bonn Germany Ecole Polytech Fed Lausanne Lausanne Switzerland Univ Bologna Bologna Italy Open Percept Inc Santa Margarita CA USA
Registration is an important step when processing three-dimensional (3-D) point clouds. Applications for registration range from object modeling and tracking, to simultaneous localization and mapping (SLAM). This arti... 详细信息
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point Set Registration for 3D Range Scans Using Fuzzy Cluster-Based Metric and Efficient Global Optimization
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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2021年 第9期43卷 3229-3246页
作者: Liao, Qianfang Sun, Da Andreasson, Henrik Orebro Univ Ctr Appl Autonomous Sensor Syst AASS S-70281 Orebro Sweden
This study presents a new point set registration method to align 3D range scans. In our method, fuzzy clusters are utilized to represent a scan, and the registration of two given scans is realized by minimizing a fuzz... 详细信息
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Efficient scaling registration algorithm for partially overlapping point sets
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ELECTRONICS LETTERS 2013年 第20期49卷 1267-1268页
作者: Ma, Liang Zhu, Jihua Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China
An efficient iterative closest point (ICP) variant is proposed for solving the scaling registration of partially overlapping point sets. By adopting the backward distance measurement and the overlapping rate, the afor... 详细信息
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Bi-Direction ICP: Fast Registration Method of point Clouds  15
Bi-Direction ICP: Fast Registration Method of Point Clouds
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15th IAPR International Conference on Machine Vision Applications (MVA)
作者: Guan, Shiqiang Li, Guolin Xie, Xiang Wang, Zhihua Tsinghua Univ Inst Microelect Beijing Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China
iterative closest point is a vital method in registration of point clouds. It runs slowly in case of large point clouds. In this article, we propose the Bi-Direction ICP in a 2D plane. The rotation model is used to co... 详细信息
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