• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

通过比较对应点处变换后的法线增强超级4PCS算法。

Enhanced Super4PCS Algorithm by Comparing Transformed Normals at Corresponding Points.

作者信息

Liu Hai, Wang Shulin, Zhao Donghong

机构信息

Mechanical School of Jiangsu University, Zhenjiang 212013, Jiangsu Province, China.

Yangzhou Polytechnic Institute, Yangzhou 225127, Jiangsu Province, China.

出版信息

Comput Intell Neurosci. 2022 Mar 30;2022:6513776. doi: 10.1155/2022/6513776. eCollection 2022.

DOI:10.1155/2022/6513776
PMID:35401712
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8986415/
Abstract

In this paper, an enhanced algorithm based on the Super4PCS algorithm was established to address the problem of prolonged congruent set verification of Super4PCS for point clouds with many points or low overlap. By comparing normals of corresponding points in a source point cloud and a tentatively transformed target point cloud, this approach dramatically decreases the time required for candidate transformation verification. This strategy has been shown to improve registration efficiency in experiments.

摘要

本文提出了一种基于Super4PCS算法的改进算法，以解决Super4PCS算法在处理多点或低重叠率点云时全等集验证时间过长的问题。通过比较源点云和初步变换后的目标点云中对应点的法向量，该方法显著减少了候选变换验证所需的时间。实验表明，该策略提高了配准效率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c20e/8986415/5d2a2cc5ba9a/CIN2022-6513776.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c20e/8986415/3cadfa2105bf/CIN2022-6513776.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c20e/8986415/3425f6e8271b/CIN2022-6513776.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c20e/8986415/f6fbdc584724/CIN2022-6513776.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c20e/8986415/22ada16f73cc/CIN2022-6513776.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c20e/8986415/3fd544d04aa8/CIN2022-6513776.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c20e/8986415/b1c4146bf9b7/CIN2022-6513776.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c20e/8986415/7c3a73cf727b/CIN2022-6513776.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c20e/8986415/5d2a2cc5ba9a/CIN2022-6513776.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c20e/8986415/3cadfa2105bf/CIN2022-6513776.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c20e/8986415/3425f6e8271b/CIN2022-6513776.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c20e/8986415/f6fbdc584724/CIN2022-6513776.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c20e/8986415/22ada16f73cc/CIN2022-6513776.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c20e/8986415/3fd544d04aa8/CIN2022-6513776.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c20e/8986415/b1c4146bf9b7/CIN2022-6513776.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c20e/8986415/7c3a73cf727b/CIN2022-6513776.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c20e/8986415/5d2a2cc5ba9a/CIN2022-6513776.008.jpg

相似文献

1
Enhanced Super4PCS Algorithm by Comparing Transformed Normals at Corresponding Points.通过比较对应点处变换后的法线增强超级4PCS算法。
Comput Intell Neurosci. 2022 Mar 30;2022:6513776. doi: 10.1155/2022/6513776. eCollection 2022.
2
Colored Point Cloud Registration by Depth Filtering.基于深度滤波的彩色点云配准
Sensors (Basel). 2021 Oct 23;21(21):7023. doi: 10.3390/s21217023.
3
2PNS++ point-cloud registration via hash of invariants and local compatibility check.通过不变量哈希和局部兼容性检查进行 2PNS++ 点云配准。
PLoS One. 2023 Nov 16;18(11):e0287134. doi: 10.1371/journal.pone.0287134. eCollection 2023.
4
Fast Method of Registration for 3D RGB Point Cloud with Improved Four Initial Point Pairs Algorithm.基于改进的四点初始对算法的三维 RGB 点云快速配准方法。
Sensors (Basel). 2019 Dec 24;20(1):138. doi: 10.3390/s20010138.
5
Registration of 3D point clouds using a local descriptor based on grid point normal.基于网格点法线的局部描述符对三维点云进行配准。
Appl Opt. 2021 Oct 1;60(28):8818-8828. doi: 10.1364/AO.437477.
6
Automatic Registration of TLS-TLS and TLS-MLS Point Clouds Using a Genetic Algorithm.使用遗传算法对TLS-TLS和TLS-MLS点云进行自动配准
Sensors (Basel). 2017 Aug 29;17(9):1979. doi: 10.3390/s17091979.
7
An Iterative Closest Points Algorithm for Registration of 3D Laser Scanner Point Clouds with Geometric Features.一种用于具有几何特征的三维激光扫描仪点云配准的迭代最近点算法。
Sensors (Basel). 2017 Aug 11;17(8):1862. doi: 10.3390/s17081862.
8
Fast Descriptors and Correspondence Propagation for Robust Global Point Cloud Registration.快速描述符和对应传播的鲁棒全局点云配准。
IEEE Trans Image Process. 2017 Aug;26(8):3614-3623. doi: 10.1109/TIP.2017.2700727. Epub 2017 May 3.
9
Point cloud registration from local feature correspondences-Evaluation on challenging datasets.基于局部特征对应关系的点云配准——在具有挑战性数据集上的评估
PLoS One. 2017 Nov 14;12(11):e0187943. doi: 10.1371/journal.pone.0187943. eCollection 2017.
10
Automatic point cloud registration algorithm based on the feature histogram of local surface.基于局部曲面特征直方图的自动点云配准算法。
PLoS One. 2020 Sep 11;15(9):e0238802. doi: 10.1371/journal.pone.0238802. eCollection 2020.

本文引用的文献

1
Point Set Registration With Similarity and Affine Transformations Based on Bidirectional KMPE Loss.基于双向KMPE损失的相似性和仿射变换点集配准
IEEE Trans Cybern. 2021 Mar;51(3):1678-1689. doi: 10.1109/TCYB.2019.2944171. Epub 2021 Feb 17.
2
Point set registration: coherent point drift.点集配准：相干点漂移。
IEEE Trans Pattern Anal Mach Intell. 2010 Dec;32(12):2262-75. doi: 10.1109/TPAMI.2010.46.
3
Precision range image registration using a robust surface interpenetration measure and enhanced genetic algorithms.
使用稳健的表面互穿度量和改进的遗传算法进行精确距离图像配准。
IEEE Trans Pattern Anal Mach Intell. 2005 May;27(5):762-76. doi: 10.1109/TPAMI.2005.108.