Advertisement

An Optimized Method for 3D Body Scanning Applications Based on KinectFusion

  • Faraj AlhwarinEmail author
  • Stefan Schiffer
  • Alexander Ferrein
  • Ingrid Scholl
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1024)

Abstract

KinectFusion is a powerful method for 3D reconstruction of indoor scenes. It uses a Kinect camera and tracks camera motion in real-time by applying ICP method on successive captured depth frames. Then it merges depth frames according their positions into a 3D model. Unfortunately the model accuracy is not sufficient for body scanner applications because the sensor depth noise affects the camera motion tracking and deforms the reconstructed model. In this paper we introduce a modification of the KinectFusion method for specific 3D body scanning applications. Our idea is based on the fact that, most body scanners are designed so that the camera trajectory is a fixed circle in the 3D space. Therefore each camera position can be determined as a rotation angle around a fixed axis (rotation axis) passing through a fixed point (rotation center). Because the rotation axis and the rotation center are always fixed, they can be estimated offline while filtering out depth noise through averaging many depth frames. The rotation angle can be also precisely measured by equipping the scanner motor with an angle sensor.

Keywords

Specified KinectFusion Body scanner 3D reconstruction 

References

  1. 1.
    Furukawa, Y., Ponce, J.: Accurate, dense, and robust multi-view stereopsis. In: IEEE Computer Society (CVPR) (2007)Google Scholar
  2. 2.
    Hernández, C., Vogiatzis, G., Cipolla, R.: Probabilistic visibility for multi-view stereo. In: IEEE Computer Society (CVPR) (2007)Google Scholar
  3. 3.
    W. Changchang, W.: Towards linear-time incremental structure from motion. In: International Conference 3D Vision, pp. 127–134 (2013)Google Scholar
  4. 4.
    Moulon, P., Monasse, P., Marlet, R.: Global fusion of relative motions for robust, accurate and scalable structure from motion. In: Proceedings of ICCV (2013)Google Scholar
  5. 5.
    Ni, K., Steedly, D., Dellaert, F.: Out-of-core bundle adjustment for large-scale 3D reconstruction (2007)Google Scholar
  6. 6.
    Yu, W., Zhang, H.: 3D reconstruction of indoor scenes based on feature and graph optimization. In: International Conference on Virtual Reality and Visualization (ICVRV) (2016)Google Scholar
  7. 7.
    Schoeps, T., Engel, J., Cremers, D.: Semi-dense visual odometry for AR on a smartphone. In: ISMAR (2014)Google Scholar
  8. 8.
    Davison, A.J., Reid, I.D., Molton, N., Stasse, O.: MonoSLAM: real-time single camera SLAM. IEEE Trans. Pattern Anal. Mach. Intell. 29, 1052–1067 (2007)CrossRefGoogle Scholar
  9. 9.
    Negre, P.L., Bonin-Font, F., Oliver, G.: Cluster-based loop closing detection for underwater SLAM in feature-poor regions. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 2589–2595 (2016)Google Scholar
  10. 10.
    Engel, J., Stückler, J., Cremers, D.: Large-scale direct SLAM with stereo cameras. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS) (2015)Google Scholar
  11. 11.
    Kerl, C., Sturm, J., Cremers, D.: Dense visual SLAM for RGB-D cameras. In: Proceedings of the International Conference on Intelligent Robot Systems (IROS) (2013)Google Scholar
  12. 12.
    Fioraio, N., Di Stefano, L.: SlamDunk: affordable real-time RGB-D SLAM. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8925, pp. 401–414. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-16178-5_28CrossRefGoogle Scholar
  13. 13.
    Newcombe, R., et al.: KinectFusion: real-time dense surface mapping and tracking. In: Proceedings of IEEE International Symposium on Mixed and Augmented Reality (2011)Google Scholar
  14. 14.
    Izadi, S., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of UIST, pp. 559–568 (2011)Google Scholar
  15. 15.
    Kainz, B., et al.: OmniKinect: real-time dense volumetric data acquisition and applications. In: VRST, pp. 25–32 (2012)Google Scholar
  16. 16.
    Whelan, T., Kaess, M., Fallon, M.F., Johannsson, H., Leonard, J.J., McDonald, J.: Kintinuous: spatially Extended KinectFusion. In: RSS Workshop on RGB-D: Advanced Reasoning with Depth Cameras (2012)Google Scholar
  17. 17.
    Afzal, H., et al.: Kinect deform: enhanced 3D reconstruction of non-rigidly deforming objects. In: 3DV (Workshops), pp. 7–13 (2014)Google Scholar
  18. 18.
    Pagliari, D., Menna, F., Roncella, R., Remondino, F., Pinto, L.: Kinect fusion improvement using depth camera calibration. In: The Technical Commission V Symposium Remote Sensing Spatial and Information Science, pp. 23–25 (2014)CrossRefGoogle Scholar
  19. 19.
    Jia, S., Li, B., Zhang, G., Li, X.: Improved KinectFusion based on graph-based optimization and large loop model. In: IEEE International Conference on Information and Automation (ICIA) (2016)Google Scholar
  20. 20.
    Alhwarin, F., Schiffer, S., Ferrein, A., Scholl, I.: Optimized KinectFusion algorithm for 3D scanning applications. In: Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOIMAGING 2018) (2018)Google Scholar
  21. 21.
    Arya, S., Mount, D.M., Netanyahu, N.S., Silverman, R., Wu, A.Y.: Registering multiview range data to create 3D computer objects. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 17, 820–824 (1995)CrossRefGoogle Scholar
  22. 22.
    Yang, C., Gerard, M.: Object modelling by registration of multiple range images. Image Vis. Comput. 10, 145–155 (1992)CrossRefGoogle Scholar
  23. 23.
    Wiedemeyer, T.: IAI Kinect2. Institute for Artificial Intelligence, University Bremen (2015). https://github.com/code-iai/iai_kinect2

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Faraj Alhwarin
    • 1
    Email author
  • Stefan Schiffer
    • 1
  • Alexander Ferrein
    • 1
  • Ingrid Scholl
    • 1
  1. 1.Mobile Autonomous Systems and Cognitive Robotics Institute (MASCOR)FH Aachen University of Applied SciencesAachenGermany

Personalised recommendations