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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Butkiewicz, T.: Low-cost coastal mapping using kinect v2 time-of-flight cameras. In: Oceans-St. John’s, pp. 1–9. IEEE (2014)
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)
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)
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)
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)
Sell, J., O’Connor, P.: The xbox one system on a chip and kinect sensor. In: IEEE Micro, pp. 44–53 (2014)
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
(OpenNI) http://www.openni.org
Blake, J., Echtler, F., Kerl, C.: (libfreenect2). https://github.com/OpenKinect/libfreenect2
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)
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)
x rite: ColorChecker Classic. (http://xritephoto.com/ph_product_overview.aspx ?ID=1192). Accessed 2016
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)
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
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-54427-4_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-54426-7
Online ISBN: 978-3-319-54427-4
eBook Packages: Computer ScienceComputer Science (R0)