Research on Fast Loading Large Scale Point Cloud Data File

被引:0
|
作者
Zhang, Jiansheng [1 ]
机构
[1] Southwest Univ Sci & Technol, Sch Mfg Sci & Engn, Mianyang 621010, Peoples R China
关键词
Point Cloud; Fast Loading; Large Scale; Memory Pre-allocation; Multiple-point Concurrency Writing;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
To meet the requirement of fast loading large scale point could data file, proposed a new method based on memory pre-allocation and multiple-point concurrency writing technologies which depend on thread pool and memory mapping file mechanism under Windows platform. Test result shows that the new method based on memory pre-allocation and multiple-point concurrency writing technologies improve the performance of loading large-scale point-could data file notably with 220%similar to 300% in loading efficiency, and along with the file size increment, the extra loading time caused by the method is the least compared with the other three methods.
引用
收藏
页码:398 / 406
页数:9
相关论文
共 50 条
  • [1] Research on Large-scale Point Cloud Data Processing Algorithm for Simulated Container Loading Test
    Li, Rui
    Liao, Lei
    Wang, Ji
    Liu, Yujun
    Sun, Ruixue
    Wang, Wei
    Ship Building of China, 2023, 64 (06) : 192 - 203
  • [2] Research on fast simplification algorithm of point cloud data
    Huang, Yuan
    Da, Feipeng
    Tang, Lin
    Yu, Jian
    Deng, Xing
    Gai, Shaoyan
    FIFTH INTERNATIONAL CONFERENCE ON OPTICAL AND PHOTONICS ENGINEERING, 2017, 10449
  • [3] RESEARCH ON THE INCOMPLETE POINT CLOUD DATA REPAIRING OF THE LARGE-SCALE SCENE BUILDINGS
    Li, Yongqiang
    Li, Lixue
    Niu, Lubiao
    Huang, Tengda
    Li, Youpeng
    2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2016, : 6726 - 6729
  • [4] A Robust and Fast Reconstruction Framework for Noisy and Large Point Cloud Data
    Feng, Xiang
    Yu, Xiaoqing
    Wan, Wanggen
    Pfaender, Fabien
    Alfredo Sanchez, J.
    2014 14TH IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND GRID COMPUTING (CCGRID), 2014, : 828 - 836
  • [5] DDRNet: Fast point cloud registration network for large-scale scenes
    Zhang, Zhenghua
    Chen, Guoliang
    Wang, Xuan
    Shu, Mingcong
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2021, 175 : 184 - 198
  • [6] Design of Point Cloud Data Structures for Efficient Processing of Large-Scale Point Clouds
    Wang, Yixuan
    Li, Xudong
    Zhao, Fenglin
    Jin, Zhehui
    Tang, Yong
    Zhao, Huijie
    INTERNATIONAL CONFERENCE ON OPTICAL AND PHOTONIC ENGINEERING, ICOPEN 2023, 2024, 13069
  • [7] Research on large scale data processing technology based on cloud computing
    Wen Qiuhua
    PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND MANAGEMENT INNOVATION, 2015, 28 : 720 - 723
  • [8] MITIGATION OF LARGE-SCALE RDF DATA LOADING WITH THE EMPLOYMENT OF A CLOUD COMPUTING SERVICE
    Namgoong, Hyun
    Kumar, Harshit
    Kim, Hong-Gee
    KEOD 2010: Proceedings of the International Conference on Knowledge Engineering and Ontology Development, 2010, : 489 - 492
  • [9] Scale Variable Fast Global Point Cloud Registration
    Zhang C.-Y.
    Wei Z.-Z.
    Xu H.-W.
    Chen Y.-S.
    Wang G.-P.
    Jisuanji Xuebao/Chinese Journal of Computers, 2019, 42 (09): : 1939 - 1952
  • [10] Fast Cylinder Shape Matching Using Random Sample Consensus in Large Scale Point Cloud
    Jin, Young-Hoon
    Lee, Won-Hyung
    APPLIED SCIENCES-BASEL, 2019, 9 (05):