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3D Visual Content Creation, Coding and Delivery pp 299–325Cite as

3D Visual Content Datasets

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Part of the book series: Signals and Communication Technology ((SCT))

Abstract

Development and performance evaluation of efficient methods for coding, transmission, and quality assessment of 3D visual content require rich datasets of a suitable test material. The use of these databases allows a fair comparison of systems under test. Moreover, publicly available and widely used datasets are crucial for experimentation leading to reproducible research. This chapter presents an overview of 3D visual content datasets relevant to research in the field of coding, transmission, and quality assessment. Description of regular stereoscopic or multiview image and video datasets is presented. Databases created using emerging technologies, including light-field imaging, are also addressed. Moreover, there are databases of multimedia content annotated with ratings from the subjective experiment, which are a necessary resource for understanding the complex problem of quality of experience while consuming the 3D visual content.

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Notes

  1. 1.

    Image and Video Quality Resources (http://stefan.winklerbros.net/resources.html).

  2. 2.

    Qualinet Databases (http://dbq.multimediatech.cz/).

  3. 3.

    COST Action IC1003 QUALINET (http://www.qualinet.eu/).

  4. 4.

    COST Action IC1105 3D-ConTourNet (http://www.3d-contournet.eu/).

  5. 5.

    MPI-Sintel dataset (http://sintel.is.tue.mpg.de/).

  6. 6.

    Blender (http://www.blender.org).

  7. 7.

    MPI-Sintel stereo videos with ground truth disparity (http://sintel.is.tue.mpg.de/stereo).

  8. 8.

    MPI-Sintel ground truth depth maps (http://sintel.is.tue.mpg.de/depth).

  9. 9.

    ETH3D dataset (www.eth3d.net).

  10. 10.

    KITTI Vision Benchmark Suite (http://www.cvlibs.net/datasets/kitti/).

  11. 11.

    Middlebury dataset (http://vision.middlebury.edu/mview).

  12. 12.

    Stanford Spherical Gantry (https://graphics.stanford.edu/projects/gantry/).

  13. 13.

    Stanford 3D Scanning Repository (http://graphics.stanford.edu/data/3Dscanrep/).

  14. 14.

    CVLAB multiview evaluation dataset (https://cvlab.epfl.ch/).

  15. 15.

    CVLAB stereo face database (http://cvlab.epfl.ch/data/stereoface).

  16. 16.

    CVLAB stereo dataset of buildings (http://cvlab.epfl.ch/data/strechamvs).

  17. 17.

    CVLAB multiview car dataset (http://cvlab.epfl.ch/data/pose).

  18. 18.

    TUM—Computer Vision Group (http://vision.in.tum.de/data/datasets/3dreconstruction).

  19. 19.

    Cornell 3D Location Recognition Datasets (http://www.cs.cornell.edu/projects/p2f).

  20. 20.

    Washington University Photo Tourism Dataset (http://phototour.cs.washington.edu/datasets/).

  21. 21.

    Photometric Harvard Stereo Dataset (http://vision.seas.harvard.edu/qsfs/Data.html).

  22. 22.

    ‘DiLiGenT’ Photometric Stereo Dataset (https://sites.google.com/site/photometricstereodata/).

  23. 23.

    Stanford Computer Vision and Geometry Lab (http://cvgl.stanford.edu/resources.html).

  24. 24.

    Stanford 2D-3D-Semantics Dataset 2D-3D-S (http://buildingparser.stanford.edu/dataset.html).

  25. 25.

    Joint Photographic Experts Group (https://jpeg.org/index.html).

  26. 26.

    Moving Picture Experts Group (https://mpeg.chiariglione.org/).

  27. 27.

    JPEG Pleno (https://jpeg.org/jpegpleno/index.html).

  28. 28.

    JPEG Pleno Database (https://jpeg.org/plenodb/).

  29. 29.

    ERC-funded Interfere project (http://www.erc-interfere.eu/).

  30. 30.

    b-com hologram repository (https://hologram-repository.labs.b-com.com).

  31. 31.

    Oakland Dataset (http://www.cs.cmu.edu/~vmr/datasets/oakland_3d/cvpr09/doc/).

  32. 32.

    IRCCyN/IVC 3D Images dataset (http://ivc.univ-nantes.fr/en/databases/3D_Images/).

  33. 33.

    LIVE 3D Image Quality Database Phase I (http://live.ece.utexas.edu/research/quality/live_3dimage_phase1.html).

  34. 34.

    LIVE 3D Image Quality Database Phase II (http://live.ece.utexas.edu/research/quality/live_3dimage_phase2.html).

  35. 35.

    MMSPG 3D Image Quality Assessment Database (http://mmspg.epfl.ch/3diqa).

  36. 36.

    IRCCyN/IVC DIBR Images (http://ivc.univ-nantes.fr/en/databases/DIBR_Images/).

  37. 37.

    MCL-3D Database (http://mcl.usc.edu/mcl-3d-database/).

  38. 38.

    IRCCyN/IVC NAMA3DS1 (http://ivc.univ-nantes.fr/en/databases/NAMA3DS1_COSPAD1/).

  39. 39.

    MMSPG 3D Video Quality Assessment Database (http://mmspg.epfl.ch/cms/page-58395.html).

  40. 40.

    IRCCyN/IVC DIBR Videos (http://ivc.univ-nantes.fr/en/databases/DIBR_Videos/).

  41. 41.

    LIRIS 3D Model Masking Database (http://liris.cnrs.fr/guillaume.lavoue/data/datasets.html).

  42. 42.

    LIRIS/EPFL 3D Model General-Purpose Database (http://liris.cnrs.fr/guillaume.lavoue/data/datasets.html).

  43. 43.

    IRCCyN/IVC 3D Gaze (http://ivc.univ-nantes.fr/en/databases/3D_Gaze/).

  44. 44.

    Middlebury stereo database (http://vision.middlebury.edu/stereo/data/).

  45. 45.

    EyeC3D: 3D Video Eye-tracking Dataset (http://mmspg.epfl.ch/eyec3d).

  46. 46.

    IRCCyN/IVC Eye-tracking Database for Stereoscopic Videos (http://ivc.univ-nantes.fr/en/databases/Eyetracking_For_Stereoscopic_Videos/).

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Authors and Affiliations

  1. Department of Radioelectronics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic

    Karel Fliegel

  2. University of Roma TRE, Rome, Italy

    Federica Battisti & Marco Carli

  3. Vienna University of Technology, Vienna, Austria

    Margrit Gelautz

  4. University of Nantes, Nantes, France

    Lukáš Krasula & Patrick Le Callet

  5. Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia

    Vladimir Zlokolica

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Correspondence to Karel Fliegel .

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  1. Instituto de Telecomunicações and Politécnico de Leiria, Leiria, Portugal

    Pedro Amado Assunção

  2. Department of Signal Processing, Tampere University of Technology, Tampere, Finland

    Atanas Gotchev

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Fliegel, K. et al. (2019). 3D Visual Content Datasets. In: Assunção, P., Gotchev, A. (eds) 3D Visual Content Creation, Coding and Delivery. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-77842-6_11

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  • DOI: https://doi.org/10.1007/978-3-319-77842-6_11

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  • Online ISBN: 978-3-319-77842-6

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