Abstract
During the last three years after the launch of the Microsoft Kinect® in the end-consumer market we have become witnesses of a small revolution in computer vision research towards the use of a standardized consumer-grade RGBD sensor for scene content retrieval. Beside classical localization and motion capturing tasks the Kinect has successfully been employed for the reconstruction of opaque and transparent objects. This report gives a comprehensive overview over the main publications using the Microsoft Kinect out of its original context as a decision-forest based motion-capturing tool.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Albers, M., Berger, B.K., Magnor, E.P.D.I.M.: The capturing of turbulent gas flows using multiple kinects. Bachelor thesis, Technical University Braunschweig (2012)
Aydemir, A., Henell, D., Jensfelt, P., Shilkrot, R.: Kinect@ home: Crowdsourcing a large 3d dataset of real environments. In: 2012 AAAI Spring Symposium Series (2012)
Bartczak, B., Koch, R.: Dense depth maps from low resolution time-of-flight depth and high resolution color views. In: Bebis, G., et al. (eds.) ISVC 2009, Part II. LNCS, vol. 5876, pp. 228–239. Springer, Heidelberg (2009)
Berger, K., Ruhl, K., Albers, M., Schroder, Y., Scholz, A., Kokemuller, J., Guthe, S., Magnor, M.: The capturing of turbulent gas flows using multiple kinects. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1108–1113. IEEE (2011)
Berger, K., Ruhl, K., Brümmer, C., Schröder, Y., Scholz, A., Magnor, M.: Markerless motion capture using multiple color-depth sensors. In: Proc. Vision, Modeling and Visualization (VMV), vol. 2011, p. 3 (2011)
Van den Bergh, M., Carton, D., De Nijs, R., Mitsou, N., Landsiedel, C., Kuehnlenz, K., Wollherr, D., Van Gool, L., Buss, M.: Real-time 3D hand gesture interaction with a robot for understanding directions from humans. In: 2011 IEEE RO-MAN, pp. 357–362. IEEE (2011)
Besl, P.J., McKay, N.D.: A method for registration of 3-d shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)
Breiman, L.: Random forests. Machine learning 45(1), 5–32 (2001)
Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 2, pp. 60–65. IEEE (2005)
Butler, D.A., Izadi, S., Hilliges, O., Molyneaux, D., Hodges, S., Kim, D.: Shake’n’sense: Reducing interference for overlapping structured light depth cameras. In: Proceedings of the 2012 ACM Annual Conference on Human Factors in Computing Systems, pp. 1933–1936. ACM (2012)
Bylow, E., Sturm, J., Kerl, C., Kahl, F., Cremers, D.: Real-time camera tracking and 3d reconstruction using signed distance functions. In: Robotics: Science and Systems Conference (RSS) (2013)
Camplani, M., Salgado, L.: Efficient spatio-temporal hole filling strategy for kinect depth maps. In: International Society for Optics and Photonics, IS&T/SPIE Electronic Imaging, p. 82900E (2012)
Chen, J., Izadi, S., Fitzgibbon, A.: Kinêtre: Animating the world with the human body. In: Proceedings of the 25th Annual ACM Symposium on User Interface Software and Technology, pp. 435–444. ACM (2012)
Chen, L., Lin, H., Li, S.: Depth image enhancement for kinect using region growing and bilateral filter. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 3070–3073. IEEE (2012)
Chiu, W.C., Blanke, U., Fritz, M.: Improving the kinect by cross-modal stereo. In: 22nd British Machine Vision Conference (BMVC) (2011)
Dal Mutto, C., Zanuttigh, P., Cortelazzo, G.M.: A probabilistic approach to tof and stereo data fusion. In: 3DPVT, Paris, France (May 2010)
Danciu, G., Banu, S.M., Caliman, A.: Shadow removal in depth images morphology-based for kinect cameras. In: 2012 16th International Conference on System Theory, Control and Computing (ICSTCC), pp. 1–6. IEEE (2012)
Endres, F., Hess, J., Engelhard, N., Sturm, J., Cremers, D., Burgard, W.: An evaluation of the rgb-d slam system. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1691–1696. IEEE (2012)
Faion, F., Friedberger, S., Zea, A., Hanebeck, U.D.: Intelligent sensor-scheduling for multi-kinect-tracking. In: Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) (2012)
Fischer, J., Arbeiter, G., Verl, A.: Combination of time-of-flight depth and stereo using semiglobal optimization. In: Int. Conf. on Robotics and Automation (ICRA), pp. 3548–3553. IEEE (2011)
Frati, V., Prattichizzo, D.: Using kinect for hand tracking and rendering in wearable haptics. In: 2011 IEEE World Haptics Conference (WHC), pp. 317–321. IEEE (2011)
Girshick, R., Shotton, J., Kohli, P., Criminisi, A., Fitzgibbon, A.: Efficient regression of general-activity human poses from depth images. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 415–422. IEEE (2011)
Gottfried, J.-M., Fehr, J., Garbe, C.: Computing range flow from multi-modal kinect data. Advances in Visual Computing, 758–767 (2011)
Gudmundsson, S.A., Aanaes, H., Larsen, R.: Fusion of stereo vision and time-of-flight imaging for improved 3D estimation. IJISTA 5(3), 425–433 (2008)
Hahne, U., Alexa, M.: Combining time-of-flight depth and stereo images without accurate extrinsic calibration. IJISTA 5(3), 325–333 (2008)
Hahne, U., Alexa, M.: Depth imaging by combining time-of-flight and on-demand stereo. In: Kolb, A., Koch, R. (eds.) Dyn3D 2009. LNCS, vol. 5742, pp. 70–83. Springer, Heidelberg (2009)
Han, J., Shao, L., Xu, D., Shotton, J.: Enhanced computer vision with microsoft kinect sensor: A review. IEEE Transactions on Cybernetics (2013)
Handa, A., Newcombe, R.A., Angeli, A., Davison, A.J.: Real-Time camera tracking: when is high frame-rate best? In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VII. LNCS, vol. 7578, pp. 222–235. Springer, Heidelberg (2012), http://link.springer.com/chapter/10.1007/978-3-642-33786-4_17
Henry, P., Fox, D., Bhowmik, A., Mongia, R.: Patch Volumes: Segmentation-based Consistens Mapping with RGB-D Cameras. In: International Conference on 3D Vision 2013 (3DV) (2013)
Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D.: Rgb-d mapping: Using depth cameras for dense 3d modeling of indoor environments. In: The 12th International Symposium on Experimental Robotics (ISER), vol. 20, pp. 22–25 (2010)
Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D.: Rgb-d mapping: Using kinect-style depth cameras for dense 3d modeling of indoor environments. The International Journal of Robotics Research 31(5), 647–663 (2012)
Heredia, F., Favier, R.: Point cloud library developers blog, kinfu large scale (June 18, 2012), http://www.pointclouds.org/blog/srcs/
Daniel Herrera, C., Kannala, J., Heikkilä, J.: Accurate and practical calibration of a depth and color camera pair. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds.) CAIP 2011, Part II. LNCS, vol. 6855, pp. 437–445. Springer, Heidelberg (2011)
Hu, G., Huang, S., Zhao, L., Alempijevic, A., Dissanayake, G.: A robust rgb-d slam algorithm. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2012)
Huang, J., Lee, A.B., Mumford, D.: Statistics of range images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 324–331. IEEE (2000)
Huhle, B., Fleck, S., Schilling, A.: Integrating 3D time-of-flight camera data and high resolution images for 3Dtv applications. In: Proc. 3DTV Conf. IEEE (2007)
Huhle, B., Schairer, T., Jenke, P., Straßer, W.: Robust non-local denoising of colored depth data. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2008), pp. 1–7. IEEE (2008)
Izadi, S., Newcombe, R.A., Kim, D., Hilliges, O., Molyneaux, D., Hodges, S., Kohli, P., Shotton, J., Davison, A.J., Fitzgibbon, A.: KinectFusion: Real-time dynamic 3D surface reconstruction and interaction. In: ACM SIGGRAPH 2011 Talks, p. 23. ACM (2011)
Kate Solomon - techradar.com: Meerkats to go Ultra HD in BBC’s first 4K broadcast, http://www.techradar.com/news/tv/television/meerkats-togo-ultra-hd-in-bbcs-first-4k-broadcast-1127915/
Keller, M., Lefloch, D., Lambers, M., Izadi, S., Weyrich, T., Kolb, A.: Real-time 3D Reconstruction in Dynamic Scenes using Point-based Fusion. In: International Conference on 3D Vision 2013 (3DV) (2013)
Kerl, C., Sturm, J., Cremers, D.: Robust odometry estimation for rgb-d cameras. In: Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA) (May 2013)
Kuhnert, K., Stommel, M.: Fusion of stereo-camera and pmd-camera data for real-time suited precise 3d environment reconstruction. In: Int. Conf. on Intelligent Robots and Systems, pp. 4780–4785. IEEE (2006)
Laurentini, A.: The visual hull concept for silhouette-based image understanding. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(2), 150–162 (1994)
Lee, T., Lim, S., Lee, S., An, S., Oh, S.: Indoor mapping using planes extracted from noisy rgb-d sensors. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2012)
Lenzen, F., Schäfer, H., Garbe, C.: Denoising time-of-flight data with adaptive total variation. In: Bebis, G. (ed.) ISVC 2011, Part I. LNCS, vol. 6938, pp. 337–346. Springer, Heidelberg (2011)
Leyvand, T., Meekhof, C., Wei, Y.C., Sun, J., Guo, B.: Kinect identity: Technology and experience. Computer 44(4), 94–96 (2011)
Lysenkov, I., Eruhimov, V.: Pose refinement of transparent rigid objects with a stereo camera. In: 22th International Conference on Computer Graphics and Vision (GraphiCon 2012) (2012)
Lysenkov, I., Eruhimov, V., Bradski, G.: Recognition and pose estimation of rigid transparent objects with a kinect sensor. In: Robotics: Science and Systems VIII, Sydney, Australia (2012)
Mac Aodha, O., Campbell, N.D.F., Nair, A., Brostow, G.J.: Patch based synthesis for single depth image super-resolution. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 71–84. Springer, Heidelberg (2012)
Maimone, A., Fuchs, H.: Reducing interference between multiple structured light depth sensors using motion. In: 2012 IEEE Virtual Reality Workshops (VR), pp. 51–54. IEEE (2012)
Mardia, K., Dryden, I.: The statistical analysis of shape data. Biometrika 76(2), 271–281 (1989)
Meister, S., Izadi, S., Kohli, P., Hämmerle, M., Rother, C., Kondermann, D.: When can we use kinectfusion for ground truth acquisition? In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Workshops & Tutorials (2012)
Microsoft Corporation: Kinect for windows sdk, http://www.microsoft.com/enus/kinectforwindows/
Microsoft News Center: Microsoft press release (March 2010), http://www.microsoft.com/en-us/news/press/2010/mar10/03-31PrimeSensePR.aspx
Microsoft Xbox support: Room lighting conditions for kinect, http://support.xbox.com/en-US/xbox-360/kinect/lighting/
Nair, R., Lenzen, F., Meister, S., Schäfer, H., Garbe, C., Kondermann, D.: High accuracy TOF and stereo sensor fusion at interactive rates. In: Fusiello, A., Murino, V., Cucchiara, R. (eds.) ECCV 2012 Ws/Demos, Part II. LNCS, vol. 7584, pp. 1–11. Springer, Heidelberg (2012)
Newcombe, R.A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A.J., Kohli, P., Shotton, J., Hodges, S., Fitzgibbon, A.: KinectFusion: Real-time dense surface mapping and tracking. In: 2011 10th IEEE International Symposium on Mixed and Augmented Reality, vol. 7, pp. 127–136 (2011)
Newman, P., Ho, K.: Slam-loop closing with visually salient features. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation (ICRA 2005), pp. 635–642. IEEE (2005)
Nguyen, C.V., Izadi, S., Lovell, D.: Modeling kinect sensor noise for improved 3D reconstruction and tracking. In: Second International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), pp. 524–530. IEEE (2012)
Nowozin, S., Rother, C., Bagon, S., Sharp, T., Yao, B., Kohli, P.: Decision tree fields. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 1668–1675. IEEE (2011)
Oikonomidis, I., Kyriazis, N., Argyros, A.: Efficient model-based 3d tracking of hand articulations using kinect. BMVC (August 2, 2011)
Openkinect Project: libfreenect, http://openkinect.org/
OpenNI: Openni framework, http://www.openni.org
Raheja, J.L., Chaudhary, A., Singal, K.: Tracking of fingertips and centers of palm using kinect. In: 2011 Third International Conference on Computational Intelligence, Modelling and Simulation (CIMSiM), pp. 248–252. IEEE (2011)
Raptis, M., Kirovski, D., Hoppe, H.: Real-time classification of dance gestures from skeleton animation. In: Proceedings of the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 147–156. ACM (2011)
Roth, H., Vona, M.: Moving volume kinectfusion. In: British Machine Vision Conf. (BMVC), Surrey, UK (2012)
Ruhl, K., Klose, F., Lipski, C., Magnor, M.: Integrating approximate depth data into dense image correspondence estimation. In: Proceedings of the 9th European Conference on Visual Media Production, pp. 26–31. ACM (2012)
Schoner, H., Moser, B., Dorrington, A.A., Payne, A.D., Cree, M.J., Heise, B., Bauer, F.: A clustering based denoising technique for range images of time of flight cameras. In: 2008 International Conference on Computational Intelligence for Modelling Control & Automation, pp. 999–1004. IEEE (2008)
Schröder, Y., Berger, K., Magnor, M.: Super resolution for active light sensor enhancement. Bachelor thesis, University of Braunschweig (March 2012)
Schröder, Y., Scholz, A., Berger, K., Ruhl, K., Guthe, S., Magnor, M.: Multiple kinect studies. Computer Graphics (2011)
Schnauer, C., Kaufmann, H.: Wide area motion tracking using consumer hardware. In: Proceedings of Workshop on Whole Body Interaction in Games and Entertainment, Advances in Computer Entertainment Technology (ACE 2011), Lisbon, Portugal (2011)
Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-time human pose recognition in parts from single depth images. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1297–1304. IEEE (2011)
Smisek, J., Jancosek, M., Pajdla, T.: 3d with kinect. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1154–1160. IEEE (2011)
Somanath, G., Cohen, S., Price, B., Kambhamettu, C.: Stereo+Kinect for High Resolution Stereo Correspondences. In: International Conference on 3D Vision 2013 (3DV) (2013)
Steinbrücker, F., Sturm, J., Cremers, D.: Real-time visual odometry from dense rgb-d images. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 719–722. IEEE (2011)
Stuckler, J., Behnke, S.: Integrating depth and color cues for dense multi-resolution scene mapping using rgb-d cameras. In: 2012 IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 162–167. IEEE (2012)
Stückler, J., Behnke, S.: Multi-resolution surfel maps for efficient dense 3D modeling and tracking. Journal of Visual Communication and Image Representation (2013)
Tam, G., Cheng, Z.Q., Lai, Y.K., Langbein, F., Liu, Y., Marshall, A., Martin, R., Sun, X.F., Rosin, P.: Registration of 3d point clouds and meshes: A survey from rigid to non-rigid. IEEE Transactions on Visualization and Computer Graphics PP(99), 1 (2012)
Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Sixth International Conference on Computer Vision, pp. 839–846. IEEE (1998)
Wang, F., Zhang, C.: Feature extraction by maximizing the average neighborhood margin. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2007), pp. 1–8. IEEE (2007)
Weickert, J.: Anisotropic diffusion in image processing, vol. 1. Teubner Stuttgart (1998)
Whelan, T., Johannsson, H., Kaess, M., Leonard, J.J., McDonald, J.: Robust real-time visual odometry for dense rgb-d mapping. In: IEEE Intl. Conf. on Robotics and Automation (ICRA), Karlsruhe, Germany (2013)
Whelan, T., Kaess, M., Fallon, M., Johannsson, H., Leonard, J., McDonald, J.: Kintinuous: Spatially extended kinectfusion. Technical Report MIT-CSAIL-TR-2012-020, CSAIL Technical Reports (2012), http://hdl.handle.net/1721.1/71756
Woodford, O., Torr, P., Reid, I., Fitzgibbon, A.: Global stereo reconstruction under second-order smoothness priors. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(12), 2115–2128 (2009)
Xu, K., Zhou, J., Wang, Z.: A method of hole-filling for the depth map generated by kinect with moving objects detection. In: 2012 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), pp. 1–5. IEEE (2012)
Yang, C., Medioni, G.: Object modelling by registration of multiple range images. Image and Vision Computing 10(3), 145–155 (1992)
Zeng, M., Zhao, F., Zheng, J., Liu, X.: A memory-efficient kinectFusion using octree. In: Hu, S.-M., Martin, R.R. (eds.) CVM 2012. LNCS, vol. 7633, pp. 234–241. Springer, Heidelberg (2012)
Zhu, J., Wang, L., Yang, R., J., Davis, J., et al.: Reliability fusion of time-of-flight depth and stereo for high quality depth maps. TPAMIÂ (99), 1 (2011)
Zollhöfer, M., Martinek, M., Greiner, G., Stamminger, M., Süßmuth, J.: Automatic reconstruction of personalized avatars from 3D face scans. Computer Animation and Virtual Worlds 22(2-3), 195–202 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Berger, K., Meister, S., Nair, R., Kondermann, D. (2013). A State of the Art Report on Kinect Sensor Setups in Computer Vision. In: Grzegorzek, M., Theobalt, C., Koch, R., Kolb, A. (eds) Time-of-Flight and Depth Imaging. Sensors, Algorithms, and Applications. Lecture Notes in Computer Science, vol 8200. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44964-2_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-44964-2_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-44963-5
Online ISBN: 978-3-642-44964-2
eBook Packages: Computer ScienceComputer Science (R0)