Kinect@Home: A Crowdsourced RGB-D Dataset

  • Rasmus Göransson
  • Alper Aydemir
  • Patric Jensfelt
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 302)


Algorithms for 3D localization, mapping, and reconstruction are getting increasingly mature. It is time to also make the datasets on which they are tested more realistic to reflect the conditions in the homes of real people. Today algorithms are tested on data gathered in the lab or at best in a few places, and almost always by the people that designed the algorithm. In this paper, we present the first RGB-D dataset from the crowdsourced data collection project Kinect@Home and perform an initial analysis of it. The dataset contains 54 recordings with a total of approximately 45 min of RGB-D video. We present a comparison of two different pose estimation methods, the Kinfu algorithm and a key point-based method, to show how this dataset can be used even though it is lacking ground truth. In addition, the analysis highlights the different characteristics and error modes of the two methods and shows how challenging data from the real world is.


RGB-D Dataset Reconstruction SLAM Benchmark 



The authors would like to thank Daniel Henell, Max Roth, and Mihai Damaschin.


  1. 1.
    Aydemir, A., Henell, D., Jensfelt, P., Shilkrot, R.: Kinect@home: crowdsourcing a large 3d dataset of real environments. In: AAAI spring symposium series (2012)Google Scholar
  2. 2.
    Ceriani, S., Fontana, G., Giusti, A., Marzorati, D., Matteucci, M., Migliore, D., Rizzi, D., Sorrenti, D.G., Taddei, P.: Rawseeds ground truth collection systems for indoor self-localization and mapping. Auton. Robots 27(4), 353–371 (2009)Google Scholar
  3. 3.
    Curless, B., Levoy, M.: A volumetric method for building complex models from range images. In: Proceedings of the 23rd annual conference on Computer graphics and interactive techniques. pp. 303–312. ACM (1996)Google Scholar
  4. 4.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: A Large-Scale Hierarchical Image Database. In: CVPR09 (2009)Google Scholar
  5. 5.
    Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. International Journal of Computer Vision 88(2), 303–338 (Jun 2010)Google Scholar
  6. 6.
    Göransson, R.: Automatic 3D reconstruction with Kinect : A modular system for creating high quality light weight textured meshes from rgbd video. Master’s thesis, KTH Royal Institute of Technology, School of Computer Science and Communication (2013)Google Scholar
  7. 7.
    Guivant, J., Nebot, E., Baiker, S.: Autonomous navigation and map building using laser range sensors in outdoor applications. Journal of robotic systems 17(10), 565–583 (2000)Google Scholar
  8. 8.
    Howard, A., Roy, N.: The robotics data set repository (radish) (2003),
  9. 9.
    Janoch, A., Karayev, S., Jia, Y., Barron, J.T., Fritz, M., Saenko, K., Darrell, T.: A category-level 3d object dataset: Putting the kinect to work. In: Consumer Depth Cameras for Computer Vision (CDC4CV) workshop, pp. 141–165. Springer (2011)Google Scholar
  10. 10.
    Kuemmerle, R., Grisetti, G., Strasdat, H., Konolige, K., Burgard, W.: g2o: A general framework for graph optimization. In: Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA) (2011)Google Scholar
  11. 11.
    Lorensen, W.E., Cline, H.E.: Marching cubes: A high resolution 3d surface construction algorithm. In: ACM Siggraph Computer Graphics. vol. 21, pp. 163–169. ACM (1987)Google Scholar
  12. 12.
    Newcombe, R., Davison, A., Izadi, S., Kohli, P., Hilliges, O., Shotton, J., Molyneaux, D., Hodges, S., Kim, D., Fitzgibbon, A.: Kinectfusion: Real-time dense surface mapping and tracking. In: Mixed and Augmented Reality (ISMAR), 2011 10th IEEE International Symposium on. pp. 127–136. IEEE (2011)Google Scholar
  13. 13.
    Rusu, R.B., Cousins, S.: 3d is here: Point cloud library (pcl). In: International Conference on Robotics and Automation. Shanghai, China (2011 2011)Google Scholar
  14. 14.
    Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of rgb-d slam systems. In: Proc. of the International Conference on Intelligent Robot Systems (IROS) (Oct 2012)Google Scholar
  15. 15.
    Vondrick, C., Patterson, D., Ramanan, D.: Efficiently scaling up crowdsourced video annotation. International Journal of Computer Vision pp. 1–21, Scholar
  16. 16.
    Whelan, T., McDonald, J., Kaess, M., Fallon, M., Johannsson, H., Leonard, J.J.: Kintinuous: Spatially extended kinectfusion. Tech. Rep. MIT-CSAIL-TR-2012-020, Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, Massachusetts (July 2012),

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Rasmus Göransson
    • 1
  • Alper Aydemir
    • 2
  • Patric Jensfelt
    • 1
  1. 1.Centre for Autonomous SystemsKTH Royal Institute of TechnologyStockholmSweden
  2. 2.Computer Vision Group, Jet Propulsion Laboratory, NASALos AngelesUSA

Personalised recommendations