skip to main content
10.1145/2047196.2047270acmconferencesArticle/Chapter ViewAbstractPublication PagesuistConference Proceedingsconference-collections
research-article

KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera

Authors Info & Claims
Published:16 October 2011Publication History

ABSTRACT

KinectFusion enables a user holding and moving a standard Kinect camera to rapidly create detailed 3D reconstructions of an indoor scene. Only the depth data from Kinect is used to track the 3D pose of the sensor and reconstruct, geometrically precise, 3D models of the physical scene in real-time. The capabilities of KinectFusion, as well as the novel GPU-based pipeline are described in full. Uses of the core system for low-cost handheld scanning, and geometry-aware augmented reality and physics-based interactions are shown. Novel extensions to the core GPU pipeline demonstrate object segmentation and user interaction directly in front of the sensor, without degrading camera tracking or reconstruction. These extensions are used to enable real-time multi-touch interactions anywhere, allowing any planar or non-planar reconstructed physical surface to be appropriated for touch.

References

  1. P. J. Besl and N. D. McKay. A method for registration of 3D shapes. IEEE Trans. Pattern Anal. Mach. Intell., 14:239--256, February 1992. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. X. Cao and R. Balakrishnan. Interacting with dynamically defined information spaces using a handheld projector and a pen. In UIST, pages 225--234, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Y. Chen and G. Medioni. Object modeling by registration of multiple range images. Image and Vision Computing (IVC), 10(3):145--155, 1992. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Y. Cui et al. 3d shape scanning with a time-of-flight camera. In Computer Vision and Pattern Recognition (CVPR), pages 1173 --1180, June 2010.Google ScholarGoogle ScholarCross RefCross Ref
  5. B. Curless and M. Levoy. A volumetric method for building complex models from range images. ACM Trans. Graph., 1996.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. S. Farsiu et al. Fast and robust multiframe super resolution. IEEE Transactions on Image Processing, 13(10):1327--1344, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. J. Frahm et al. Building Rome on a cloudless day. In Proc. Europ. Conf. on Computer Vision (ECCV), 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. B. Freedman, A. Shpunt, M. Machline, and Y. Arieli. Depth Mapping Using Projected Patterns. Patent Application, 10 2008. WO 2008/120217 A2.Google ScholarGoogle Scholar
  9. S. L. Grand. Broad-phase collision detection with CUDA. In GPU Gems 3. Addison-Wesley, 2007.Google ScholarGoogle Scholar
  10. T. Harada. Real-time rigid body simulation on gpus. In GPU Gems 3. Addison-Wesley Professional, 2007.Google ScholarGoogle Scholar
  11. R. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, second edition, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. P. Henry et al. RGB-D mapping: Using depth cameras for dense 3D modeling of indoor environments. In Proc. of the Int. Symposium on Experimental Robotics (ISER), 2010.Google ScholarGoogle Scholar
  13. B. Huhle et al. Fusion of range and color images for denoising and resolution enhancement with a non-local filter. Computer Vision and Image Understanding, 114(12):1336--1345, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. M. Kazhdan, M. Bolitho, and H. Hoppe. Poisson surface reconstruction. In Proc. of the Eurographics Symposium on Geometry Processing, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. G. Klein and D. W. Murray. Parallel tracking and mapping for small AR workspaces. In ISMAR, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. M. Levoy et al. The digital Michelangelo Project: 3D scanning of large statues. ACM Trans. Graph., 2000.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. K. Low. Linear least-squares optimization for point-to-plane icp surface registration. Technical report, TR04-004, University of North Carolina, 2004.Google ScholarGoogle Scholar
  18. P. Merrell et al. Real-time visibility-based fusion of depth maps. In Proc. of the Int. Conf. on Computer Vision (ICCV), 2007.Google ScholarGoogle ScholarCross RefCross Ref
  19. R. A. Newcombe and A. J. Davison. Live dense reconstruction with a single moving camera. In Proc. of the IEEE (CVPR), 2010.Google ScholarGoogle ScholarCross RefCross Ref
  20. R. A. Newcombe, S. Lovegrove, and A. J. Davison. Dense tracking and mapping in real-time. In Proc. of the Int. Conf. on Computer Vision (ICCV), 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. R. A. Newcombe et al. Real-Time Dense Surface Mapping and Tracking with Kinect. In ISMAR, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. S. Osher and R. Fedkiw. Level Set Methods and Dynamic Implicit Surfaces. Springer, 2002.Google ScholarGoogle Scholar
  23. S. Rusinkiewicz, O. Hall-Holt, and M. Levoy. Real-time 3D model acquisition. ACM Trans. Graph., 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. S. Rusinkiewicz and M. Levoy. Efficient variants of the ICP algorithm. 3D Digital Imaging and Modeling, Int. Conf. on, 0:145, 2001.Google ScholarGoogle Scholar
  25. S. Thrun. Robotic mapping: A survey. In Exploring Artificial Intelligence in the New Millenium. 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. D. Vlasic et al. Dynamic shape capture using multi-view photometric stereo. ACM Trans. Graph., 28(5), 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. D. Wagner, T. Langlotz, and D. Schmalstieg. Robust and unobtrusive marker tracking on mobile phones. In ISMAR, pages 121--124, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. T. Weise, T. Wismer, B. Leibe, and L. V. Gool. In-hand scanning with online loop closure. In IEEE Int. Workshop on 3-D Digital Imaging and Modeling, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  29. K. Zhou, M. Gong, X. Huang, and B. Guo. Data-parallel octrees for surface reconstruction. IEEE Trans. on Visualization and Computer Graphics, 17, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        UIST '11: Proceedings of the 24th annual ACM symposium on User interface software and technology
        October 2011
        654 pages
        ISBN:9781450307161
        DOI:10.1145/2047196

        Copyright © 2011 ACM

        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]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 16 October 2011

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        UIST '11 Paper Acceptance Rate67of262submissions,26%Overall Acceptance Rate842of3,967submissions,21%

        Upcoming Conference

        UIST '24

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader