Hand Waving Away Scale

  • Christopher Ham
  • Simon Lucey
  • Surya Singh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8692)


This paper presents a novel solution to the metric reconstruction of objects using any smart device equipped with a camera and an inertial measurement unit (IMU). We propose a batch, vision centric approach which only uses the IMU to estimate the metric scale of a scene reconstructed by any algorithm with Structure from Motion like (SfM) output. IMUs have a rich history of being combined with monocular vision for robotic navigation and odometry applications. These IMUs require sophisticated and quite expensive hardware rigs to perform well. IMUs in smart devices, however, are chosen for enhancing interactivity - a task which is more forgiving to noise in the measurements. We anticipate, however, that the ubiquity of these “noisy” IMUs makes them increasingly useful in modern computer vision algorithms. Indeed, we show in this work how an IMU from a smart device can help a face tracker to measure pupil distance, and an SfM algorithm to measure the metric size of objects. We also identify motions that produce better results, and develop a heuristic for estimating, in real-time, when enough data has been collected for an accurate scale estimation.


Smart devices IMU metric 3D reconstruction 


  1. 1.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press (2004) ISBN: 0521540518Google Scholar
  2. 2.
    Nützi, G., Weiss, S., Scaramuzza, D., Siegwart, R.: Fusion of IMU and vision for absolute scale estimation in monocular slam. Journal of Intelligent & Robotic Systems 61(1-4), 287–299 (2011)CrossRefGoogle Scholar
  3. 3.
    Weiss, S., Achtelik, M.W., Lynen, S., Achtelik, M.C., Kneip, L., Chli, M., Siegwart, R.: Monocular vision for long-term micro aerial vehicle state estimation: A compendium. Journal of Field Robotics 30(5), 803–831 (2013)CrossRefGoogle Scholar
  4. 4.
    Jung, S.H., Taylor, C.J.: Camera trajectory estimation using inertial sensor measurements and structure from motion results. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. II–732. IEEE (2001)Google Scholar
  5. 5.
    Skoglund, M., Sjanic, Z., Gustafsson, F.: Initialisation and estimation methods for batch optimisation of inertial/visual slam. Technical report, Department of Electrical Engineering Linköpings Universitet (2013)Google Scholar
  6. 6.
    Jones, E., Vedaldi, A., Soatto, S.: Inertial structure from motion with autocalibration. In: Workshop on Dynamical Vision (2007)Google Scholar
  7. 7.
    Li, M., Kim, B.H., Mourikis, A.I.: Real-time motion tracking on a cellphone using inertial sensing and a rolling-shutter camera. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 4712–4719 (2013)Google Scholar
  8. 8.
    Smart Tools co.: Smart Measure Pro. Google Play Store (2013),
  9. 9.
    Kamens, B.: RulerPhone - Photo Measuring. Apple App Store (2010–2013),
  10. 10.
    Tanskanen, P., Kolev, K., Meier, L., Camposeco, F., Saurer, O., Pollefeys, M.: Live metric 3d reconstruction on mobile phones (2013)Google Scholar
  11. 11.
    Konolige, K., Agrawal, M., Solà, J.: Large-scale visual odometry for rough terrain. In: Kaneko, M., Nakamura, Y. (eds.) Robotics Research. STAR, vol. 66, pp. 201–212. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Aggarwal, P., Syed, Z., Niu, X., El-Sheimy, N.: A standard testing and calibration procedure for low cost mems inertial sensors and units. Journal of Navigation 61(02), 323–336 (2008)CrossRefGoogle Scholar
  13. 13.
    Android Documentation: SensorEvent - Android Developers (2013),
  14. 14.
    iOS Documentation: UIAcceleration Class Reference (2010),
  15. 15.
    Cox, M.J., Nuevo, J.S., Lucey, S.: Deformable model fitting by regularized landmark mean-shift. International Journal of Computer Vision (IJCV) 91(2), 200–215 (2011)CrossRefGoogle Scholar
  16. 16.
    Rosner, B.: Percentage points for a generalized ESD many-outlier procedure. Technometrics 25(2), 165–172 (1983)CrossRefzbMATHGoogle Scholar
  17. 17.
    van den Hengel, A., Dick, A., Thormählen, T., Ward, B., Torr, P.H.: Videotrace: rapid interactive scene modelling from video. ACM Transactions on Graphics (TOG) 26, 86 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Christopher Ham
    • 1
  • Simon Lucey
    • 2
  • Surya Singh
    • 1
  1. 1.Robotics Design LabThe University of QueenslandAustralia
  2. 2.Robotics InstituteCarnegie Melon UniversityUSA

Personalised recommendations