Research on point-cloud collection and 3D model reconstruction

被引:0
|
作者
Sheng, Jianan [1 ]
Zhang, Jian [1 ]
Mi, Hong [2 ]
Ye, Maosheng [3 ]
机构
[1] Tongji Univ, Sch Mech Engn, Shanghai, Peoples R China
[2] LIANZHOU REFRIGERANTS CO LTD, Quzhou, Zhejiang, Peoples R China
[3] GAOMING ANNWA CERAM SANIT WARE CO LTD, Guangzhou, Peoples R China
基金
国家重点研发计划;
关键词
point-cloud collection; 3D model reconstruction; multi-line structured light; SHAPE; CALIBRATION;
D O I
10.1109/iecon43393.2020.9255086
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Point-cloud collection is used to collect 3D surface features from the object. 3D reconstruction can form a visual 3D model based on point- cloud. They are the important parts of 3D surface measurement. 3D surface measurement can effectively help enterprises shorten the design cycle, improve product quality, save labor costs, and improve the competitiveness of enterprises. Optical image measurement is a branch of 3D surface measurement. Because optical image measurement has the advantages of non-contact, high speed, high degree of automation and good flexibility, it has been researched and applied widely. Image processing and calibration technology are often used in optical image measurement. Image processing can extract valuable information from images, and calibration technology is necessary for mathematical model. Multi-line structured light has been widely used in the measurement.
引用
收藏
页码:5331 / 5336
页数:6
相关论文
共 50 条
  • [31] Spectral-GANs for High-Resolution 3D Point-cloud Generation
    Ramasinghe, Sameera
    Khan, Salman
    Barnes, Nick
    Gould, Stephen
    2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2020, : 8169 - 8176
  • [32] Unsupervised detection of vineyards by 3D point-cloud UAV photogrammetry for precision agriculture
    Comba, Lorenzo
    Biglia, Alessandro
    Aimonino, Davide Ricauda
    Gay, Paolo
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2018, 155 : 84 - 95
  • [33] Target Detection from 3D Point-Cloud using Gaussian Function and CNN
    Liu, ShuaiXin
    Zheng, JianYing
    Wang, Xiang
    Zhang, ZhenYao
    Sun, RongChuan
    2019 34RD YOUTH ACADEMIC ANNUAL CONFERENCE OF CHINESE ASSOCIATION OF AUTOMATION (YAC), 2019, : 562 - 567
  • [34] Edge-Oriented Point-Cloud Transformer for 3D Intracranial Aneurysm Segmentation
    Liu, Yifan
    Liu, Jie
    Yuan, Yixuan
    MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT V, 2022, 13435 : 97 - 106
  • [35] Continuous plane detection in point-cloud data based on 3D Hough Transform
    Hulik, Rostislav
    Spanel, Michal
    Smrz, Pavel
    Materna, Zdenek
    JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2014, 25 (01) : 86 - 97
  • [36] Point-Cloud Method for Automated 3D Coronary Tree Reconstruction From Multiple Non-Simultaneous Angiographic Projections
    Banerjee, Abhirup
    Galassi, Francesca
    Zacur, Ernesto
    De Maria, Giovanni Luigi
    Choudhury, Robin P.
    Grau, Vicente
    IEEE TRANSACTIONS ON MEDICAL IMAGING, 2020, 39 (04) : 1278 - 1290
  • [37] 3D Point Cloud Denoising and Normal Estimation for 3D Surface Reconstruction
    Liu, Chang
    Yuan, Ding
    Zhao, Hongwei
    2015 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (ROBIO), 2015, : 820 - 825
  • [38] 3D Building Scene Reconstruction Based on 3D LiDAR Point Cloud
    Yang, Shih-Chi
    Fan, Yu-Cheng
    2017 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS - TAIWAN (ICCE-TW), 2017,
  • [39] Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models
    Xu, Chenfeng
    Yang, Shijia
    Galanti, Tomer
    Wu, Bichen
    Yue, Xiangyu
    Zhai, Bohan
    Zhan, Wei
    Vajda, Peter
    Keutzer, Kurt
    Tomizuka, Masayoshi
    COMPUTER VISION, ECCV 2022, PT XXXVII, 2022, 13697 : 638 - 656
  • [40] Robust 3D Model Reconstruction Based on Continuous Point Cloud for Autonomous Vehicles
    Gao, Hongwei
    Yu, Jiahui
    Sun, Jian
    Yang, Wei
    Jiang, Yueqiu
    Zhu, Lei
    Ju, Zhaojie
    SENSORS AND MATERIALS, 2021, 33 (09) : 3169 - 3186