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International Conference on Social Robotics

ICSR 2018: Social Robotics pp 380–389Cite as

Feature-Based Monocular Dynamic 3D Object Reconstruction

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11357))

Abstract

Dynamic 3D object reconstruction becomes increasingly crucial to various intelligent applications. Most existing algorithms, in spite of the accurate performances, have the problems of high cost and complex computations. In this paper, we propose a novel framework for dynamic 3D object reconstruction with a single camera in an attempt to address this problem. The gist of the proposed approach is to reduce the reconstruction problem to a pose estimation problem. We reconstruct the whole object by estimating the poses of its topological segmentations. Experiments are undertaken to validate the effectiveness of the proposed method in comparison with several state-of-art methods.

This work was jointly supported by National Natural Science Foundation of China (Grant No. U1613210) and Shenzhen Fundamental Research Programs (JCYJ20170413165528221, JCYJ2016428154842603).

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References

  1. Kahn, S., Bockholt, U., Kuijper, A., Fellner, D.W.: Towards precise real-time 3D difference detection for industrial applications. Comput. Ind. 64, 1115–1128 (2013)

    Article  Google Scholar 

  2. Chiew, W.M., Lin, F., Qian, K., Seah, H.S.: A heterogeneous computing system for coupling 3D endomicroscopy with volume rendering in real-time image visualization. Comput. Ind. 65, 367–381 (2014)

    Article  Google Scholar 

  3. Lee, J.J., Lee, B.G., Yoo, H.: Depth extraction of three-dimensional objects using block matching for slice images in synthetic aperture integral imaging. Appl. Opt. 50, 5624–5629 (2011)

    Article  Google Scholar 

  4. Saracchini, R.F.V., Stolfi, J., Leitao, H.C.G., Atkinson, G.A., Smith, M.L.: Robust 3D face capture using example-based photometric stereo. Comput. Ind. 64, 1399–1410 (2013)

    Article  Google Scholar 

  5. Wang, M., Hong, R., Yuan, X.T., Yan, S., Chua, T.S.: Movie2Comics: towards a lively video content presentation. IEEE Trans. Multimedia 14, 858–870 (2012)

    Article  Google Scholar 

  6. Choi, W., Fang, Y.C., Badizadegan, K., Dasari, R.R., Feld, M.S.: Extended depth of focus in tomographic phase microscopy using a propagation algorithm. Opt. Lett. 33, 171–173 (2008)

    Article  Google Scholar 

  7. Guillemaut, J.Y., Hilton, A.: Joint multi-layer segmentation and reconstruction for free-viewpoint video applications. Int. J. Comput. Vis. (IJCV) 93, 73–100 (2010)

    Article  Google Scholar 

  8. Chiu, W.C., Fritz, M.: Multi-class video co-segmentation with a generative multi-video model. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2013)

    Google Scholar 

  9. Goldluecke, B., Magnor, M.: Space-time isosurface evolution for temporally coherent 3D reconstruction. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 350–355 (2004)

    Google Scholar 

  10. Vineet, V., et al.: Incremental dense semantic stereo fusion for large-scale semantic scene reconstruction. In: IEEE International Conference on Robotics and Automation (ICRA) (2015)

    Google Scholar 

  11. Schönberger, J.L., Zheng, E., Frahm, J.-M., Pollefeys, M.: Pixelwise view selection for unstructured multi-view stereo. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 501–518. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_31

    Chapter  Google Scholar 

  12. Langguth, F., Sunkavalli, K., Hadap, S., Goesele, M.: Shading-aware multi-view stereo. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 469–485. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_29

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

    Yongsheng Ou

  2. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

    Shaokun Jin & Yongsheng Ou

  3. Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China

    Shaokun Jin

  4. The CAS Key Laboratory of Human-Machine-Intelligence Synergic Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

    Yongsheng Ou

Authors
  1. Shaokun Jin
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  2. Yongsheng Ou
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Corresponding author

Correspondence to Yongsheng Ou .

Editor information

Editors and Affiliations

  1. The National University of Singapore, Singapore, Singapore

    Shuzhi Sam Ge

  2. Qatar University, Doha, Qatar

    John-John Cabibihan

  3. University Carlos III de Madrid, Madrid, Spain

    Miguel A. Salichs

  4. Canterbury University, Auckland, New Zealand

    Elizabeth Broadbent

  5. Wichita State University, Wichita, KS, USA

    Hongsheng He

  6. The Pennsylvania State University, University Park, PA, USA

    Alan R. Wagner

  7. Universidad Carlos III de Madrid, Madrid, Spain

    Álvaro Castro-González

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Cite this paper

Jin, S., Ou, Y. (2018). Feature-Based Monocular Dynamic 3D Object Reconstruction. In: Ge, S., et al. Social Robotics. ICSR 2018. Lecture Notes in Computer Science(), vol 11357. Springer, Cham. https://doi.org/10.1007/978-3-030-05204-1_37

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  • DOI: https://doi.org/10.1007/978-3-030-05204-1_37

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05203-4

  • Online ISBN: 978-3-030-05204-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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