Computer Science ›› 2018, Vol. 45 ›› Issue (11A): 180-184.

• Pattem Recognition & Image Processing • Previous Articles     Next Articles

Multi-view Geometric 3D Reconstruction Method Based on AKAZE Algorithm

ZHOU Sheng-pu, GENG Guo-hua, LI Kang, WANG Piao   

  1. School of Information Science and Technology,Northwest University,Xi’an 710127,China
    National-Local Joint Engineering Research Center of Cultural Heritage Digitization,Northwest University,Xi’an 710127,China
  • Online:2019-02-26 Published:2019-02-26

Abstract: Aiming at the low efficiency of incremental motion recovery structure algorithm in multi-view geometric 3D reconstruction algorithm,a multi-view geometric 3D reconstruction method based on AKAZE algorithm was proposed.The target image obtained by the camera is detected and matched by AKAZE algorithm,and the weak matching image is eliminated by using the random sample consensus algorithm and the three view constraints.Then the global rotation parameters are solved by the least square method according to the relative position and attitude parameters of the matching graphs,and the global displacement parameters are solved by using the three-view constraint relation.Finally,the bundle adjustment optimization is carried out.The experimental results show that the proposed algorithm can improve the processing efficiency and meet the needs of fast processing on the basis of improving the reconstruction effect.

Key words: AKAZE algorithm, Global 3D reconstruction, RANSAC algorithm, Bundle adjustment

CLC Number: 

  • TP391.7
[1]CHENG X J,ZHANG H F,XIE R.Study on 3D laser scanning modeling method for Large-Scale history building[C]∥International Conference on Computer Application and System Mo-deling.IEEE,2010:V7-573-V7-577.
[2]GRACIÁ L,SAEZ-BARONA S,CARRIOÓN D,et al.A System for Real-Time Multi-View 3D Reconstruction[C]∥Workshops on Database and Expert Systems Applications.IEEE Computer Society,2010:235-239.
[3]SCHÖNING J,HEIDEMANN G.Evaluation of multi-view 3D reconstruction software[C]∥Computer Analysis of Images and Patterns.2015:450-461.
[4]HÄMING K,PETERS G.The structure-from-motion recon-struction pipeline-A survey with focus on short image sequences[J].Kybernetika-Praha,2010,5(5):926-937.
[5]SABZEVARI R,BUE A D,MURINO V.Structure from Motion and Photometric Stereo for Dense 3D Shape Recovery[M]// Image Analysis and Processing-ICIAP 2011.Springer Berlin Heidelberg,2011:660-669.
[6]SCHÖNBERGER J L,FRAHM J M.Structure-from-Motion Revisited[C]∥Computer Vision and Pattern Recognition.IEEE,2016.
[7]MOULON P,MONASSE P,MARLET R.Adaptive Structure from Motion with a Contrario,Model Estimation[M]∥Compu-ter Vision-ACCV 2012.Springer Berlin Heidelberg,2012:257-270.
[8]MOULON P,MONASSE P,MARLET R.Global Fusion of Re-lative Motions for Robust,Accurate and Scalable Structure from Motion[C]∥IEEE International Conference on Computer Vision.IEEE Computer Society,2013:3248-3255.
[9]ALCANTARILLA P F,BARTOLI A,DAVISON A J.KAZE Features[M]∥Computer Vision-ECCV 2012.Springer Berlin Heidelberg,2012:214-227.
[10]JIANG G,LIU L,ZHU W,et al.A 127 fps in full hd accelerator based on optimized AKAZE with efficiency and effectiveness for image feature extraction[C]∥IEEE Design Automation Con-ference.2015:1-6.
[11]CHEN Y,CHAN A B,LIN Z,et al.Efficient tree-structured SfM by RANSAC generalized Procrustes analysis[J].Computer Vision & Image Understanding,2017,157(C):179-189.
[14]SHI L M,GUO F S,HU Z Y,et al.An Improved PMVS through Scene Geometric Information[J].Acta Automatica Si-nica,2011,37(5):560-568.
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