3D reconstruction method based on multi-view infrared sensor

SONG Li-mei; ZHANG Ji-peng; LI Yun-peng; LIU Zhen-ning; LUO Jing

doi:10.37188/CJLCD.2023-0026

您当前的位置：

首页 >

文章列表页 >

3D reconstruction method based on multi-view infrared sensor

Intelligent Imaging | 更新时间：2023-06-12

- 3D reconstruction method based on multi-view infrared sensor
- Chinese Journal of Liquid Crystals and Displays Vol. 38, Issue 6, Pages: 759-769(2023)
- 作者机构：
  
  天津工业大学天津市电气装备智能控制重点实验室，天津 300387
- 作者简介：
- 基金信息：
  
  Program for Innovative Research Team in University of Tianjin(TD13-5036);Tianjin Science and Technology Popularization Project(22KPXMRC00090)
- DOI：10.37188/CJLCD.2023-0026
  CLC： TP391.4
- Received：31 January 2023，
  
  Revised：14 March 2023，
  
  Published：05 June 2023
- 稿件说明：
移动端阅览
SONG Li-mei, ZHANG Ji-peng, LI Yun-peng, et al. 3D reconstruction method based on multi-view infrared sensor[J]. Chinese journal of liquid crystals and displays, 2023, 38(6): 759-769.
DOI：

SONG Li-mei, ZHANG Ji-peng, LI Yun-peng, et al. 3D reconstruction method based on multi-view infrared sensor[J]. Chinese journal of liquid crystals and displays, 2023, 38(6): 759-769. DOI： 10.37188/CJLCD.2023-0026.

摘要

为了实现有特征物体和无特征物体更精准的三维重建，本文研究了多视角传感器下三维点云的自动拼接算法。首先由不同视角的传感器双目标定后进行轴线数据的标定，接着在三维空间内对多条轴线数据进行分析并提出了一种基于多视角传感器轴线融合的点云拼接方法，从而计算出误差最小的最优轴线数据，最后以拟合出的轴线数据为轴心在世界坐标系内进行三维点云的拼接。实验结果表明，在1.3~1.9 m的测量范围内，本文所提出的拼接方法对直径为144.954 2 mm的标准球进行三维重建的误差在0.037 mm以内，重建无特征点物体和有特征点物体都能有较好的拼接效果且拼接时间不受点云总量大小的限制。该拼接方法基本满足三维重建的稳定性好、效率快、精度高等要求。

Abstract

In order to realize more accurate 3D reconstruction of featureless and featureless objects， this paper studies the automatic splicing algorithm of 3D point cloud under multi-view sensor. Firstly， the axis data was calibrated by the binocular calibration of sensors from different perspectives， and then multiple axis data were analyzed in three-dimensional space. A point cloud splicing method based on the axis fusion of sensors from multiple perspectives was proposed， so as to calculate the optimal axis data with the minimum error. Finally， the fitted axis data was used as the axis center to splice the three-dimensional point cloud in the world coordinate system. The experimental results show that the error of the proposed method is less than 0.037 mm in the measurement range of 1.3~1.9 m for the standard ball with a diameter of 144.954 2 mm， and the reconstruction of objects without and with feature points can have a good stitching effect， and the stitching time is not limited by the size of the total point cloud. This method basically meets the requirements of good stability， fast efficiency and high precision of 3D reconstruction.

关键词

Keywords

references

杨佳琪，张世坤，范世超，等 . 多视图点云配准算法综述［J］. 华中科技大学学报（自然科学版）， 2022 ， 50 （ 11 ）： 16 - 34+43 .

YANG J Q ， ZHANG S K ， FAN S C ， et al . Review of multi-view point cloud registration algorithms ［J］. Journal of Central China Normal University （Natural Sciences）， 2022 ， 50 （ 11 ）： 16 - 34+43 . （in Chinese）

LI P ， WANG R ， WANG Y ， et al . Evaluation of the ICP algorithm in 3D point cloud registration ［J］. 2020 ， 8 ： 68030 - 68048 . doi: 10.1109/access.2020.2986470 http://dx.doi.org/10.1109/access.2020.2986470

MAKOVETSKII A ， VORONIN S ， KOBER V ， et al . A regularized point cloud registration approach for orthogonal transformations ［J］. Journal of Global Optimization ， 2020 ： 1 - 23 .

赵云涛，齐佳祥，李维刚，等 . 基于改进三维形状上下文的点云配准［J］. 液晶与显示， 2022 ， 37 （ 12 ）： 1590 - 1597 . doi: 10.37188/CJLCD.2022-0156 http://dx.doi.org/10.37188/CJLCD.2022-0156

ZHAO Y T ， QI J X ， LI W G ， et al . Point cloud registration based on improved 3DSC ［J］. Chinese Journal of Liquid Crystals and Displays ， 2022 ， 37 （ 12 ）： 1590 - 1597 . （in Chinese） . doi: 10.37188/CJLCD.2022-0156 http://dx.doi.org/10.37188/CJLCD.2022-0156

ZUO C ， QIAN J ， FENG S ， et al . Deep learning in optical metrology： a review ［J］. Light： Science & Applications ， 2022 ， 11 （ 1 ）： 39 . doi: 10.1038/s41377-022-00714-x http://dx.doi.org/10.1038/s41377-022-00714-x

RIVENSON Y ， WU Y ， OZCAN A . Deep learning in holography and coherent imaging ［J］. Light： Science & Applications ， 2019 ， 8 （ 1 ）： 85 . doi: 10.1038/s41377-019-0196-0 http://dx.doi.org/10.1038/s41377-019-0196-0

LI S ， YE Y ， LIU J ， et al . VPRNet： virtual points registration network for partial-to-partial point cloud registration ［J］. Remote Sensing ， 2022 ， 14 （ 11 ）： 2559 . doi: 10.3390/rs14112559 http://dx.doi.org/10.3390/rs14112559

XIN M ， LI B ， YAN X ， et al . A robust cloud registration method based on redundant data reduction using backpropagation neural network and shift window ［J］. Review of Scientific Instruments ， 2018 ， 89 （ 2 ）： 024704 . doi: 10.1063/1.4996628 http://dx.doi.org/10.1063/1.4996628

CHANG W C ， PHAM V T . 3-D point cloud registration using convolutional neural networks ［J］. Applied Sciences ， 2019 ， 9 （ 16 ）： 3273 . doi: 10.3390/app9163273 http://dx.doi.org/10.3390/app9163273

LI Y ， QIAN J ， FENG S ， et al . Deep-learning-enabled dual-frequency composite fringe projection profilometry for single-shot absolute 3D shape measurement ［J］. Opto-Electron. Adv ， 2022 ， 5 ： 210021 . doi: 10.29026/oea.2022.210021 http://dx.doi.org/10.29026/oea.2022.210021

BONNEEL N ， COEURJOLLY D . Spot： sliced partial optimal transport ［J］. ACM Transactions on Graphics （TOG）， 2019 ， 38 （ 4 ）： 1 - 13 . doi: 10.1145/3306346.3323021 http://dx.doi.org/10.1145/3306346.3323021

YAN L ， WEI P ， XIE H ， et al . A new outlier removal strategy based on reliability of correspondence graph for fast point cloud registration ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence ， 2022 . doi: 10.1109/tpami.2022.3226498 http://dx.doi.org/10.1109/tpami.2022.3226498

孟晓亮，张立晔，房超 . 基于电磁追踪系统的点云配准方法研究［J］. 液晶与显示， 2020 ， 35 （ 12 ）： 1507 - 1519 . doi: 10.37188/YJYXS20203512.1309 http://dx.doi.org/10.37188/YJYXS20203512.1309

MENG X L ， ZHANG L Y ， FANG C . Point cloud registration method based on electro-magnetic tracking system ［J］. Chinese Journal of Liquid Crystals and Displays ， 2020 ， 35 （ 12 ）： 1507 - 1519 . doi: 10.37188/YJYXS20203512.1309 http://dx.doi.org/10.37188/YJYXS20203512.1309

SHU Q ， HE X ， WANG C ， et al . Fast point cloud registration in multidirectional affine transformation ［J］. Optik ， 2021 ， 229 ： 165884 . doi: 10.1016/j.ijleo.2020.165884 http://dx.doi.org/10.1016/j.ijleo.2020.165884

DIAO S ， YANG H ， XIANG Y ， et al . Research on splicing method of point cloud with insufficient features based on spatial reference ［J］. Journal of Electronic Imaging ， 2021 ， 30 （ 4 ）： 043008 . doi: 10.1117/1.jei.30.4.043008 http://dx.doi.org/10.1117/1.jei.30.4.043008

ZHOU P ， WANG Y ， XU Y ， et al . Phase-unwrapping-free 3D reconstruction in structured light field system based on varied auxiliary point ［J］. Optics Express ， 2022 ， 30 （ 17 ）： 29957 - 29968 . doi: 10.1364/oe.468049 http://dx.doi.org/10.1364/oe.468049

Views

131

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Applications of Volumetric Three-Dimensional Display in Medical Simulation and Education

Related Author

SONG Li-mei

ZHANG Ji-peng

LI YUN-peng

LIU Zhen-ning

LUO Jing

TIAN Feng

XIA Xue

Wang He

Related Institution

Tianjin Key Laboratory of Intelligent Control of Electrical Equipment，Tian Gong University

Institute of Film & TV Arts and Technology, Shanghai University

Applied Science Lab, General Electric Healthcare

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China 130033 Postal code：130033
Tel：0431-86176059 Email：yjxs@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰