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Asian Conference on Computer Vision

ACCV 2016: Computer Vision – ACCV 2016 Workshops pp 34–45Cite as

Comparison of Kinect V1 and V2 Depth Images in Terms of Accuracy and Precision

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10117))

Abstract

RGB-D cameras like the Microsoft Kinect had a huge impact on recent research in Computer Vision as well as Robotics. With the release of the Kinect v2 a new promising device is available, which will – most probably – be used in many future research. In this paper, we present a systematic comparison of the Kinect v1 and Kinect v2. We investigate the accuracy and precision of the devices for their usage in the context of 3D reconstruction, SLAM or visual odometry. For each device we rigorously figure out and quantify influencing factors on the depth images like temperature, the distance of the camera or the scene color. Furthermore, we demonstrate errors like flying pixels and multipath interference. Our insights build the basis for incorporating or modeling the errors of the devices in follow-up algorithms for diverse applications.

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References

  1. Newcombe, R.A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A.J., Kohi, P., Shotton, J., Hodges, S., Fitzgibbon, A.: Kinectfusion: real-time dense surface mapping and tracking. In: IEEE International Symposium on Mixed and Augmented Reality (ISMAR) (2011)

    Google Scholar 

  2. Wasenmüller, O., Meyer, M., Stricker, D.: Augmented reality 3d discrepancy check in industrial applications. In: IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 125–134. IEEE (2016)

    Google Scholar 

  3. Kerl, C., Sturm, J., Cremers, D.: Robust odometry estimation for RGB-D cameras. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3748–3754. IEEE (2013)

    Google Scholar 

  4. Wasenmüller, O., Meyer, M., Stricker, D.: CoRBS: comprehensive RGB-D benchmark for SLAM using kinect v2. In: IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE (2016)

    Google Scholar 

  5. Chen, C., Jafari, R., Kehtarnavaz, N.: UTD-MHAD: a multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor. In: IEEE International Conference on Image Processing (ICIP), 168–172. IEEE (2015)

    Google Scholar 

  6. Chandra, S., Chrysos, G.G., Kokkinos, I.: Surface based object detection in RGBD images. In: Proceedings of the British Machine Vision Conference (BMVC), pp. 187.1–187.13. BMVA Press (2015)

    Google Scholar 

  7. Vianello, A., Michielin, F., Calvagno, G., Sartor, P., Erdler, O.: Depth images super-resolution: an iterative approach. In: IEEE International Conference on Image Processing (ICIP), pp. 3778–3782 (2014)

    Google Scholar 

  8. Wasenmüller, O., Bleser, G., Stricker, D.: Combined bilateral filter for enhanced real-time upsampling of depth images. In: International Conference on Computer Vision Theory and Applications (2015)

    Google Scholar 

  9. Wasenmüller, O., Ansari, M.D., Stricker, D.: DNA-SLAM: dense noise aware SLAM for ToF RGB-D cameras. In: Asian Conference on Computer Vision Workshop (ACCV Workshop), Springer (2016)

    Google Scholar 

  10. Fankhauser, P., Bloesch, M., Rodriguez, D., Kaestner, R., Hutter, M., Siegwart, R.: Kinect v2 for mobile robot navigation: Evaluation and modeling. In: International Conference on Advanced Robotics (ICAR) (2015)

    Google Scholar 

  11. Lachat, E., Macher, H., Mittet, M., Landes, T., Grussenmeyer, P.: First experiences with kinect v2 sensor for close range 3d modelling. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS) (2015)

    Google Scholar 

  12. Butkiewicz, T.: Low-cost coastal mapping using kinect v2 time-of-flight cameras. In: Oceans-St. John’s, pp. 1–9. IEEE (2014)

    Google Scholar 

  13. Fürsattel, P., Placht, S., Balda, M., Schaller, C., Hofmann, H., Maier, A., Riess, C.: A comparative error analysis of current time-of-flight sensors. IEEE Trans. Comput. Imaging 2, 27–41 (2016)

    Article  MathSciNet  Google Scholar 

  14. Samir, M., Golkar, E., Rahni, A.A.A.: Comparison between the kinect v1 and kinect v2 for respiratory motion tracking. In: IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pp. 150–155. IEEE (2015)

    Google Scholar 

  15. Amon, C., Fuhrmann, F., Graf, F.: Evaluation of the spatial resolution accuracy of the face tracking system for kinect for windows v1 and v2. In: Proceedings of the 6th Congress of the Alps Adria Acoustics Association (2014)

    Google Scholar 

  16. Zennaro, S., Munaro, M., Milani, S., Zanuttigh, P., Bernardi, A., Ghidoni, S., Menegatti, E.: Performance evaluation of the 1st and 2nd generation kinect for multimedia applications. In: 2015 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE (2015)

    Google Scholar 

  17. Sell, J., O’Connor, P.: The xbox one system on a chip and kinect sensor. In: IEEE Micro, pp. 44–53 (2014)

    Google Scholar 

  18. Lefloch, D., Nair, R., Lenzen, F., Schäfer, H., Streeter, L., Cree, M.J., Koch, R., Kolb, A.: Technical foundation and calibration methods for time-of-flight cameras. In: Grzegorzek, M., Theobalt, C., Koch, R., Kolb, A. (eds.) Time-of-Flight and Depth Imaging. Sensors, Algorithms, and Applications. LNCS, vol. 8200, pp. 3–24. Springer, Heidelberg (2013). doi:10.1007/978-3-642-44964-2_1

    Chapter  Google Scholar 

  19. (OpenNI) http://www.openni.org

  20. Blake, J., Echtler, F., Kerl, C.: (libfreenect2). https://github.com/OpenKinect/libfreenect2

  21. Rufli, M., Scaramuzza, D., Siegwart, R.: Automatic detection of checkerboards on blurred and distorted images. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3121–3126. IEEE(2008)

    Google Scholar 

  22. Lepetit, V., Moreno-Noguer, F., Fua, P.: EPnP: an accurate O(n) solution to the PnP problem. Int. J. Comput. Vis. (IJCV) 81, 155–166 (2009)

    Article  Google Scholar 

  23. x rite: ColorChecker Classic. (http://xritephoto.com/ph_product_overview.aspx ?ID=1192). Accessed 2016

  24. Gottfried, J., Nair, R., Meister, S., Garbe, C., Kondermann, D.: Time of flight motion compensation revisited. In: IEEE International Conference on Image Processing (ICIP), pp. 5861–5865. IEEE (2014)

    Google Scholar 

  25. Freedman, D., Smolin, Y., Krupka, E., Leichter, I., Schmidt, M.: SRA: fast removal of general multipath for ToF sensors. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 234–249. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10590-1_16

    Google Scholar 

  26. Cui, Y., Schuon, S., Thrun, S., Stricker, D., Theobalt, C.: Algorithms for 3d shape scanning with a depth camera. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1039–1050 (2013)

    Article  Google Scholar 

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Authors and Affiliations

  1. German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany

    Oliver Wasenmüller & Didier Stricker

Authors
  1. Oliver Wasenmüller
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  2. Didier Stricker
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Correspondence to Oliver Wasenmüller .

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Editors and Affiliations

  1. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Chu-Song Chen

  2. Tsinghua University, Beijing, China

    Jiwen Lu

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kai-Kuang Ma

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Wasenmüller, O., Stricker, D. (2017). Comparison of Kinect V1 and V2 Depth Images in Terms of Accuracy and Precision. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10117. Springer, Cham. https://doi.org/10.1007/978-3-319-54427-4_3

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  • DOI: https://doi.org/10.1007/978-3-319-54427-4_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54426-7

  • Online ISBN: 978-3-319-54427-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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