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Restoring surfaces after removing objects in indoor 3D point clouds

Published: 05 December 2013 Publication History
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  • Abstract

    In this paper, we present fast approaches for object segmentation and surface restoration of indoor 3D point clouds, which are the results of 3D reconstruction methods or range scanners. These two problems are significant in constructing a augmented reality system using a range camera to build a virtual environment and provide the interaction mechanisms to the virtual model. For point-cloud segmentation, we apply a density-based clustering algorithm to extract the desired object after removing its ground planes. This low-complexity method gives stable results with high accuracy. After the segmented object has been removed, a restoration algorithm is proposed in such a case that the holes on the ground plane are revealed by removed objects. These holes are there because the corresponding surfaces are hidden by the segmented objects in the scanner phase. The process of filling the holes includes an object-ground-plane detection, a geometric restoration and a color fusion step. The newly added points are directly interpolated from the existing object points, which cover the holes in the original point clouds. Our approaches are experimented through a variety of test datasets and yield promising results.

    References

    [1]
    J. L. Bentley. Multidimensional binary search trees used for associative searching. Commun. ACM, 18(9): 509--517, Sept. 1975.
    [2]
    Y. Boykov and V. Kolmogorov. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell., 26(9): 1124--1137, Sept. 2004.
    [3]
    Y. Y. Boykov and M. P. Jolly. Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images. volume 1, pages 105--112, 2001.
    [4]
    J. Davis, S. R. Marschner, M. Garr, and M. Levoy. Filling holes in complex surfaces using volumetric diffusion. In 3DPVT, pages 428--438. IEEE Computer Society, 2002.
    [5]
    M. Ester, H. P. Kriegel, J. Sander, and X. Xu. A Density-Based algorithm for discovering clusters in large spatial databases with noise. In Second International Conference on Knowledge Discovery and Data Mining, pages 226--231, Portland, Oregon, 1996. AAAI Press.
    [6]
    M. A. Fischler and R. C. Bolles. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM, 24(6): 381--395, June 1981.
    [7]
    D. Levin. The approximation power of moving least-squares. Math. Comput., 67(224): 1517--1531, Oct. 1998.
    [8]
    Y. Li, J. Sun, C.-K. Tang, and H.-Y. Shum. Lazy snapping. In ACM SIGGRAPH 2004 Papers, SIGGRAPH '04, pages 303--308, New York, NY, USA, 2004. ACM.
    [9]
    J. MacQueen. Some methods for classification and analysis of multivariate observations. In Proc. Fifth Berkeley Symp. on Math. Statist. and Prob., volume 1, pages 281--297. Univ. of Calif. Press, 1967.
    [10]
    R. A. Newcombe, A. J. Davison, S. Izadi, P. Kohli, O. Hilliges, J. Shotton, D. Molyneaux, S. Hodges, D. Kim, and A. Fitzgibbon. KinectFusion: Real-time dense surface mapping and tracking. In 2011 10th IEEE International Symposium on Mixed and Augmented Reality, pages 127--136. IEEE, Oct. 2011.
    [11]
    J. Nickolls, I. Buck, M. Garland, and K. Skadron. Scalable parallel programming with cuda. Queue, 6(2): 40--53, Mar. 2008.
    [12]
    D. Pham, N. H. Doan, T. B. Dinh, and T. B. Dinh. An interactive system building 3d environment using a moving depth sensor. In Computational Science and Its Applications (ICCSA) 2013, volume 7975 of Lecture Notes in Computer Science, pages 391--406. Springer Berlin Heidelberg, 2013.
    [13]
    R. B. Rusu and S. Cousins. 3D is here: Point Cloud Library (PCL). In 2011 IEEE International Conference on Robotics and Automation (ICRA), pages 1--4. IEEE, May 2011.
    [14]
    J. Shi and J. Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8): 888--905, 2000.
    [15]
    J. Siek, L.-Q. Lee, and A. Lumsdaine. Boost random number library. http://www.boost.org/libs/graph/, June 2000.
    [16]
    J. Wang and M. M. Oliveira. A hole-filling strategy for reconstruction of smooth surfaces in range images. In SIBGRAPI, pages 11--18. IEEE Computer Society, 2003.

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    1. Restoring surfaces after removing objects in indoor 3D point clouds

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        cover image ACM Other conferences
        SoICT '13: Proceedings of the 4th Symposium on Information and Communication Technology
        December 2013
        345 pages
        ISBN:9781450324540
        DOI:10.1145/2542050
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Sponsors

        • SOICT: School of Information and Communication Technology - HUST
        • NAFOSTED: The National Foundation for Science and Technology Development
        • ACM Vietnam Chapter: ACM Vietnam Chapter
        • Danang Univ. of Technol.: Danang University of Technology

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 05 December 2013

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        Author Tags

        1. 3D reconstruction
        2. DBSCAN
        3. clustering
        4. depth
        5. graph-cuts
        6. holes filling
        7. segmentation
        8. sensor

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        • Research-article

        Funding Sources

        • Advanced Program in Computer Science, University of Science, Vietnam National University - Ho Chi Minh City

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        SoICT '13
        Sponsor:
        • SOICT
        • NAFOSTED
        • ACM Vietnam Chapter
        • Danang Univ. of Technol.

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        SoICT '13 Paper Acceptance Rate 40 of 80 submissions, 50%;
        Overall Acceptance Rate 147 of 318 submissions, 46%

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