吉林大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (3): 909-918.doi: 10.13229/j.cnki.jdxbgxb20170146

• Orginal Article • Previous Articles     Next Articles

Real-time surface reconstruction based global camera pose optimization

LIN Jin-hua1, WANG Yan-jie2, WANG Lu1, YAO Yu3   

  1. 1.School of Application Technology, Changchun University of Technology, Changchun 130012,China;
    2. Chinese Academy of Sciences, Changchun Institute of Optics, Fine Mechanics and Physics,Changchun 130033, China;
    3.College of Mechanical Science and Engineering, Jilin University, Changchun 130012,China
  • Received:2017-02-23 Online:2018-05-20 Published:2018-05-20

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Abstract: An end-to-end online large-scale 3D scene reconstruction method is proposed. This method uses robustness to estimate the rotation attitude of the camera and constructs a hierarchical optimization framework for the fusion of depth data input. Then, the information of each frame is optimized according to the global pose of the camera, and the algorithm limits the target tracking time and completes real-time tracking of the frame. Experimental results show that the average time to reconstruct the algorithm reaches 399 ms and the average number of estimated Iterative Closest Point (ICP) times is 20, which needs 100 ms to complete each frame transformation. The system is robust to the reconstruction of large-scale scenes and has better real-time performance. This method is a real-time three-dimensional reconstruction system with corresponding sparseness, dense structure information and camera illustration uniformity.

Key words: computer application, machine vision, 3D reconstruction, online volume fusion, pose estimation

CLC Number: 

  • TP391
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