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Characterization of Time-of-Flight Data

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Time-of-Flight Cameras

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

Abstract

This chapter introduces the principles and difficulties of time-of-flight depth measurement. The depth images that are produced by time-of-flight cameras suffer from characteristic problems, which are divided into the following two classes. First, there are systematic errors, such as noise and ambiguity, which are directly related to the sensor. Second, there are nonsystematic errors, such as scattering and motion blur, which are more strongly related to the scene content. It is shown that these errors are often quite different from those observed in ordinary color images. The case of motion blur, which is particularly problematic, is examined in detail. A practical methodology for investigating the performance of depth cameras is presented. Time-of-flight devices are compared to structured-light systems, and the problems posed by specular and translucent materials are investigated.

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References

  1. Bartczak, B., Koch, R.: Dense depth maps from low resolution time-of-flight depth and high resolution color views. In: Proceedings of the International Symposium on Visual Computing (ISVC), pp. 228–239. Las Vegas (2009)

    Google Scholar 

  2. Chan, D., Buisman, H., Theobalt, C., Thrun, S.: A noise-aware filter for real-time depth upsampling. In: ECCV Workshop on Multi-camera and Multi-modal Sensor Fusion Algorithms and Applications (2008)

    Google Scholar 

  3. Chen, T., Lensch, H.P.A., Fuchs, C., Seidel H.P.: Polarization and phase-shifting for 3D scanning of translucent objects. In: Proceedings of the Computet Vision and, Pattern Recognition, pp. 1–8 (2007)

    Google Scholar 

  4. Choi, O., Lim, H., Kang, B., Kim, Y., Lee, K., Kim, J., Kim, C.: Range unfolding for time-of-flight depth cameras. In: Proceedings of the International Conference on Image Processing. pp. 4189–4192 (2010)

    Google Scholar 

  5. Criminisi, A., Perez, P., Toyama, K.: Region filling and object removal by exemplar-based image inpainting. IEEE Trans. Image Process. 13(9), 1200–1212 (2004)

    Article  Google Scholar 

  6. Curless, B., Levoy, M.: A volumetric method for building complex models from range images. In: Proceedings of ACM SIGGRAPH ’96, pp. 303–312 (1996)

    Google Scholar 

  7. Dolson, J., Baek, J., Plagemann, C., Thrun, S.: Fusion of time-of-flight depth and stereo for high accuracy depth maps. In: Proceedings of the Computer Vision and Pattern Pecognition (CVPR), pp. 1–8 (2008)

    Google Scholar 

  8. Dolson, J., Baek, J., Plagemann, C., Thrun, S.: Upsampling range data in dynamic environments. In: Proceedings of the Computer Vision and Pattern Recognition (CVPR), pp. 1141–1148 (2010)

    Google Scholar 

  9. Du, H., Oggier, T., Lustenberger, F., Charbon, E.: A virtual keyboard based on true-3d optical ranging. In: Proceedings of the British Machine Vision Conference (BMVC’05), pp. 220–229 (2005)

    Google Scholar 

  10. Edeler, T., Ohliger, K., Hussmann, S. Mertins, A.: Time-of-flight depth image denoising using prior noise information. In: Proceedings of the IEEE 10th International Conference on Signal Processing (ICSP), pp. 119–122 (2010)

    Google Scholar 

  11. Foix, S., Alenya, G., Torras, C.: Lock-in time-of-flight (ToF) cameras: a survey. IEEE Sens. J. 11(9), 1917–1926 (2011)

    Article  Google Scholar 

  12. Freedman, B., Shpunt, A., Machline, M., Arieli, Y.: Depth Mapping Using Projected Patterns. US Patent No. 8150412 (2012)

    Google Scholar 

  13. Fuchs, S.: Multipath interference compensation in time-of-flight camera images. In: Proceedings of the 2010 20th International Conference on, Pattern Recognition (ICPR). pp. 3583–3586 (2010)

    Google Scholar 

  14. Fuchs, S., Hirzinger, G.: Extrinsic and depth calibration of ToF-cameras. In: Proceedings of the Computer Vision and Pattern Recognition (CVPR), pp. 1–6 (2008)

    Google Scholar 

  15. Gupta, M., Agrawal, A., Veeraraghavan, A., Narasimhan, S.G.: Structured light 3D scanning under global illumination. In: Proceedings of the Computer Vision and Pattern Recognition (CVPR) (2011)

    Google Scholar 

  16. Hansard, M., Horaud, R., Amat, M., Lee, S.: Projective alignment of range and parallax data. In: Proceedings of the Computer Vision and Pattern Recognition (CVPR), pp. 3089–3096 (2011)

    Google Scholar 

  17. Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D.: RGB-D mapping: using depth cameras for dense 3d modeling of indoor environments. In: RGB-D: Advanced Reasoning with Depth Cameras Workshop in Conjunction with RSS (2010)

    Google Scholar 

  18. Huhle, B., Schairer, T., Jenke, P., Strasser. W.: Robust non-local denoising of colored depth data. In: Proceedings of the Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 1–7 (2008)

    Google Scholar 

  19. Hussmann, S., Hermanski, A., Edeler, T.: Real-time motion artifact suppression in Tof camera systems. IEEE Trans. Instrum. Meas. 60(5), 1682–1690 (2011)

    Google Scholar 

  20. Kang, B., Kim, S., Lee, S., Lee, K., Kim, J., Kim, C.: Harmonic distortion free distance estimation in Tof camera. In: SPIE Electronic Imaging (2011)

    Google Scholar 

  21. Khoshelham, K.: Accuracy analysis of kinect depth data. In: Proceedings of the ISPRS Workshop on Laser Scanning (2011)

    Google Scholar 

  22. Kim, Y., Chan, D., Theobalt, C., Thrun, S.: Design and calibration of a multi-view TOF sensor fusion system. In: Proceedings of the IEEE CVPR Workshop on Time-of-Flight Camera Based Computer Vision (2008)

    Google Scholar 

  23. Kolb, A., Barth, E., Koch, R., Larsen, R.: Time-of-flight cameras in computer graphics. Comput. Graph Forum 29(1), 141–159 (2010)

    Article  Google Scholar 

  24. Lee, S., Kang, B., Kim, J.D.K., Kim, C.-Y.: Motion blur-free time-of-flight range sensor. In: Proceedings of the SPIE Electronic Imaging (2012)

    Google Scholar 

  25. Lee, S., Shim, H., Kim, J.D.K., Kim, C.-Y.: Tof depth image motion blur detection using 3D blur shape models. In: Proceedings of the SPIE Electronic Imaging (2012)

    Google Scholar 

  26. Lindner, M., Kolb, A.: Compensation of motion artifacts for time-of-flight cameras. In: Kolb, A., Koch, R. (eds.) Dynamic 3D Imaging, Lecture Notes in Computer Science, vol. 5742, pp. 16–27. Springer, Berlin (2009)

    Google Scholar 

  27. Lindner, M., Kolb, A., Ringbeck, T.: New insights into the calibration of tof-sensors. In: Proceedings on Computer Vision and Pattern Recognition Workshops, pp. 1–5 (2008)

    Google Scholar 

  28. Lottner, O., Sluiter, A., Hartmann, K., Weihs, W.: Movement artefacts in range images of time-of-flight cameras. In: International Symposium on Signals, Circuits and Systems (ISSCS), vol. 1, pp. 1–4 (2007)

    Google Scholar 

  29. Mesa Imaging AG. http://www.mesa-imaging.ch

  30. Matyunin, S., Vatolin, D., Berdnikov, Y., Smirnov, M.: Temporal filtering for depth maps generated by kinect depth camera. In: Proceedings of the 3DTV, pp. 1–4 (2011)

    Google Scholar 

  31. May, S., Werner, B., Surmann, H., Pervolz, K.: 3D Time-of-flight cameras for mobile robotics. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 790–795 (2006)

    Google Scholar 

  32. Mure-Dubois, J., Hugli, H.: Real-time scattering compensation for time-of-flight camera. In: Proceedings of Workshop on Camera Calibration Methods for Computer Vision Systems (CCMVS2007) (2007)

    Google Scholar 

  33. 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. IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 1–8 (2011)

    Google Scholar 

  34. Park, J., Kim, H., Tai, Y.-W., Brown, M.-S., Kweon, I.S.: High quality depth map upsampling for 3D-TOF cameras. In: Proceedings of IEEE International Conference on Computer Vision (ICCV) (2011)

    Google Scholar 

  35. Reynolds, M., Dobos, J., Peel, L., Weyrich, T., Brostow, G.: Capturing time-of-flight data with confidence. In: Proceedings of the Computer Vision and Pattern Recognition (CVPR), pp. 945–952 (2011)

    Google Scholar 

  36. Ryden, F., Chizeck, H., Kosari, S.N., King, H., Hannaford, B.: Using kinect and a haptic interface for implementation of real-time virtual fixtures. In: RGB-D: Advanced Reasoning with Depth Cameras Workshop in Conjunction with RSS (2010)

    Google Scholar 

  37. Schamm, T., Strand, M., Gumpp, T., Kohlhaas, R., Zollner, J., Dillmann, R.: Vision and Tof-based driving assistance for a personal transporter. In: Proceedings of the International Conference on Advanced Robotics (ICAR), pp. 1–6 (2009)

    Google Scholar 

  38. Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vision 47, 7–42 (2002)

    Article  MATH  Google Scholar 

  39. Scharstein, D., Szeliski, R.: High-accuracy stereo depth maps using structured light. In: Proceedings of the Computer Vision and Pattern Recognition (CVPR) (2003)

    Google Scholar 

  40. Schuon, S., Theobalt, C., Davis, J., Thrun, S.: High-quality scanning using time-of-flight depth superresolution. In: Proceedings of the Computer Vision and Pattern Recognition Workshops, pp. 1–7 (2008)

    Google Scholar 

  41. Seitz, S., Curless, B., Diebel, J., Scharstein, D., Szeliski, R.: A comparison and evaluation of multi-view stereo reconstruction algorithms. In: Proceedings of the Computer Vision and Pattern Recognition (CVPR), pp. 519–528 (2006)

    Google Scholar 

  42. Shim, H., Adels, R., Kim, J., Rhee, S., Rhee, T., Kim, C., Sim, J., Gross, M.: Time-of-flight sensor and color camera calibration for multi-view acquisition. In: The Visual Computer (2011)

    Google Scholar 

  43. Shotton, J., Fitzgibbon, A., Cook, M., Blake, A.: Real-time human pose recognition in parts from single depth images. In: Proceedings of the Computer Vision and Pattern Recognition (CVPR) (2011)

    Google Scholar 

  44. Smisek, J., Jancosek, M., Pajdla, T.: 3D with kinect. In: Proceedings of International Conference on Computer Vision Workshops, pp. 1154–1160 (2011)

    Google Scholar 

  45. Soutschek, S., Penne, J., Hornegger, J., Kornhuber, J.: 3-D Gesture-based scene navigation in medical imaging applications using time-of-flight cameras. In: Proceedings of the Computer Vision and Pattern Recognition Workshops, pp. 1–6 (2008)

    Google Scholar 

  46. Tai, Y.-W., Kong, N., Lin, S., Shin, S.Y.: Coded exposure imaging for projective motion deblurring. In: Proceedings of the Computer Vision and Pattern Recognition (CVPR), pp. 2408–2415 (2010)

    Google Scholar 

  47. Whyte, O., Sivic, J., Zisserman, A., Ponce, J.: Non-uniform deblurring for shaken images. In: Proceedings of the Computer Vision and Pattern Recognition (CVPR), pp. 491–498 (2010)

    Google Scholar 

  48. Yang, Q., Yang, R., Davis, J., Nister, D.: Spatial-depth super resolution for range images. In: Proceedings of the Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2007)

    Google Scholar 

  49. Yeo, D., ul Haq, E., Kim, J., Baig, M., Shin, H.: Adaptive bilateral filtering for noise removal in depth upsampling. In: International SoC Design Conference (ISOCC), pp. 36–39 (2011)

    Google Scholar 

  50. Yuan, F., Swadzba, A., Philippsen, R., Engin, O., Hanheide, M., Wachsmuth, S.: Laser-based navigation enhanced with 3D time-of-flight data. In: Proceedings of the International Conference on Robotics and Automation (ICRA’09), pp. 2844–2850 (2009)

    Google Scholar 

  51. Zhang, L., Deshpande, A., Chen, X.: Denoising vs. deblurring: hdr imaging techniques using moving cameras. In: Proceedings of the Computer Vision and Pattern Recognition (CVPR), pp. 522–529 (2010)

    Google Scholar 

  52. Zhang, Z.: Flexible camera calibration by viewing a plane from unknown orientations. In: Proceedings of the International Conference on Computer Vision (ICCV) (1999)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Electronic Engineering and Computer Science, Queen Mary, University of London, Mile End Road, London, E1 4NS, UK

    Miles Hansard

  2. Samsung Advanced Institute of Technology, Mt. 14-1, Nongseo-dong, Giheung-gu, Yongin-si, 446-712, Kyonggi-do, Korea, Republic of (South Korea)

    Seungkyu Lee & Ouk Choi

  3. INRIA Grenoble Rhône-Alpes, avenue de l’Europe 655, Montbonnot Saint-Martin, 38330, France

    Radu Horaud

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  1. Miles Hansard
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  2. Seungkyu Lee
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  4. Radu Horaud
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Correspondence to Miles Hansard .

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© 2013 Miles Hansard

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Hansard, M., Lee, S., Choi, O., Horaud, R. (2013). Characterization of Time-of-Flight Data. In: Time-of-Flight Cameras. SpringerBriefs in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-4658-2_1

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  • DOI: https://doi.org/10.1007/978-1-4471-4658-2_1

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4657-5

  • Online ISBN: 978-1-4471-4658-2

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