LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Cai, Ziyun; Han, Jungong; Liu, Li; Shao, Ling (2017)
Publisher: Springer Nature
Journal: Multimedia Tools and Applications
Languages: English
Types: Article
Subjects: G400, Media Technology, Software, Computer Networks and Communications, Hardware and Architecture

Classified by OpenAIRE into

ACM Ref: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, ComputingMethodologies_COMPUTERGRAPHICS
RGB-D data has turned out to be a very useful representation of an indoor scene for solving fundamental computer vision problems. It takes the advantages of the color image that provides appearance information of an object and also the depth image that is immune to the variations in color, illumination, rotation angle and scale. With the invention of the low-cost Microsoft Kinect sensor, which was initially used for gaming and later became a popular device for computer vision, high quality RGB-D data can be acquired easily. In recent years, more and more RGB-D image/video datasets dedicated to various applications have become available, which are of great importance to benchmark the state-of-the-art. In this paper, we systematically survey popular RGB-D datasets for different applications including object recognition, scene classification, hand gesture recognition, 3D-simultaneous localization and mapping, and pose estimation. We provide the insights into the characteristics of each important dataset, and compare the popularity and the difficulty of those datasets. Overall, the main goal of this survey is to give a comprehensive description about the available RGB-D datasets and thus to guide researchers in the selection of suitable datasets for evaluating their algorithms.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • lccu .45 from g o 0 e in t ∼ e ll d v % eg % 5 % iv % a % -n %
    • t om 15 tra 56 .03 13 ac 70 s 6 n 3
    • m 3 o 3 m t
    • a 1 u ti m t
    • a u ti 9 n 3 8
    • 5 6 7 N c 1 6 2
    • e jtsecb tireego titiicv leeop leeop o a a p p 5 c 0 7 7 3 6 1 1 2
    • th ,th tree ,th tree th th ,th th ,th tree taa litisaedo ,lreoodp ,lreoodp lrecceom ,lreoodp ,lteenko lrecceom ,lredoop ,lredoop ,lreoopd lteekon ,lreoopd ,lredoop lrcceeom D m C C a C s a C C C s C C a 1. Abdallah D, Charpillet F (2015) Pose estimation for a partially observable human body from rgb-d
    • cameras. In: International Conference on Intelligent Robots and Systems, p 8 2. Aldoma A, Tombari F, Di Stefano L, Vincze M (2012) A global hypotheses verification method for 3d
    • object recognition. In: European Conference on Computer Vision, pp 511-524 3. Aggarwal JK, Cai Q (1997) Human motion analysis: A review. In: Nonrigid and Articulated Motion
    • Workshop, pp 90-102 4. Baltrusaitis T, Robinson P, Morency L (2012) 3d constrained local model for rigid and non-rigid facial
    • tracking. In: Conference on Computer Vision and Pattern Recognition, pp 2610-2617 5. Barbosa BI, Cristani BI, Del Bue A, Bazzani L, Murino V (2012) Re-identification with rgb-d sensors.
    • In: First International Workshop on Re-Identification, pp 433-442 6. Berger K The role of rgb-d benchmark datasets: an overview. arXiv:1310.2053 7. Brachmann E, Krull A, Michel F, Gumhold S, Shotton J, Rother C (2014) Learning 6d object pose
    • estimation using 3d object coordinates. In: European Conference on Computer Vision, pp 536-551 8. Bourke A, Obrien J, Lyons G (2007) Evaluation of a threshold-based tri-axial accelerometer fall
    • detection algorithm, Gait & posture 26(2):194-199 9. Bo L, Ren X, Fox D (2011) Depth kernel descriptors for object recognition. In: International Conference
    • on Intelligent Robots and Systems, pp 821-826 10. Bo L, Lai K, Ren X, Fox D (2011) Object recognition with hierarchical kernel descriptors. In: Conference
    • on Computer Vision and Pattern Recognition, pp 1729-1736 11. Bo L, Ren X, Fox D (2013) Unsupervised feature learning for rgb-d based object recognition. In:
    • Experimental Robotics, pp 387-402 12. Borra`s R, Lapedriza A` , Igual L (2012) Depth information in human gait analysis: an experimental study
    • on gender recognition. In: Image Analysis and Recognition, pp 98-105 13. Chen L, Wei H, Ferryman J (2013) A survey of human motion analysis using depth imagery. Pattern
    • Recogn Lett 34(15):1995-2006 14. Chen C, Jafari R, Kehtarnavaz N (2015) Utd-mad: a multimodal dataset for human action recognition
    • processing 15. Chen C, Jafari R, Kehtarnavaz N (2015) Improving human action recognition using fusion of depth
    • camera and inertial sensors. IEEE Transactions on Human-Machine Systems 45(1):51-61 16. Chen C, Jafari R, Kehtarnavaz N A real-time human action recognition system using depth and inertial
    • sensor fusion 17. Cruz L, Lucio D, Velho L (2012) Kinect and rgbd images: Challenges and applications. In: Conference
    • on Graphics, Patterns and Images Tutorials, pp 36-49 18. Chua CS, Guan H, Ho YK (2002) Model-based 3d hand posture estimation from a single 2d image.
    • Image and Vision computing 20(3):191-202 19. De Rosa R, Cesa-Bianchi N, Gori I, Cuzzolin F (2014) Online action recognition via nonparametric
    • incremental learning. In: British machine vision conference 20. Drouard V, Ba S, Evangelidis G, Deleforge A, Horaud R (2015) Head pose estimation via probabilistic
    • high-dimensional regression. In: International conference on image processing 21. Endres F, Hess J, Engelhard N, Sturm J, Cremers D, Burgard W (2012) An evaluation of the RGB-D
    • SLAM system. In: International Conference on Robotics and Automation, pp 1691-1696 22. Endres F, Hess J, Sturm J, Cremers D, Burgard W (2014) 3-D mapping with an rgb-d camera. IEEE
    • Trans Robot 30(1):177-187 23. Ellis C, Masood SZ, Tappen MF, Laviola JJ Jr, Sukthankar R (2013) Exploring the trade-off between
    • accuracy and observational latency in action recognition. Int J Comput Vis 101(3):420-436 24. Erdogmus N, Marcel S (2013) Spoofing in 2d face recognition with 3d masks and anti-spoofing with
    • kinect:1-6 25. Fanelli G, Dantone M, Gall J, Fossati A, Van Gool L (2013) Random forests for real time 3d face
    • analysis. International Journal on Computer Vision 101(3):437-458 26. Fothergill S, Mentis HM, Kohli P, Nowozin S (2012) Instructing people for training gestural interactive
    • systems. In: Conference on Human Factors in Computer Systems, pp 1737-1746 27. Garcia J, Zalevsky Z (2008) Range mapping using speckle decorrelation. United States Patent 7 433:024 28. Gasparrini S, Cippitelli E, Spinsante S, Gambi E A depth-based fall detection system using a
    • kinect®sensor, Sensors 14(2):2756-2775 29. Gao J, Ling H, Hu W, Xing J (2014) Transfer learning based visual tracking with gaussian processes
    • regression. In: European Conference on Computer Vision, pp 188-203 30. Geng J (2011) Structured-light 3d surface imaging: a tutorial. Adv Opt Photon 3(2):128-160 31. Gossow D, Weikersdorfer D, Beetz M (2012) Distinctive texture features from perspective-invariant
    • keypoints. In: International Conference on Pattern Recognition, pp 2764-2767 32. Gupta S, Girshick R, Arbela¨aez P, Malik J (2014) Learning rich features from rgb-d images for object
    • detection and segmentation. In: European Conference on Computer Vision, pp 345-360 33. Han J, Shao L, Xu D, Shotton J (2013) Enhanced computer vision with microsoft kinect sensor: a review.
    • IEEE Transactions on Cybernetics 43(5):1318-1334 34. Handa A, Whelan T, McDonald J, Davison A (2014) A benchmark for rgb-d visual odometry, 3d
    • reconstruction and slam. In: International Conference on Robotics and Automation, pp 1524-1531 35. Helmer S, Meger D, Muja M, Little JJ, Lowe DG (2011) Multiple viewpoint recognition and localization.
    • In: Asian Conference on Computer Vision, pp 464-477 36. Hinterstoisser S, Lepetit V, Ilic S, Holzer S, Bradski G, Konolige K, Navab N (2012) Model based
    • training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes, pp 548-562 37. Hinterstoisser S, Cagniart C, Ilic S, Sturm P, Navab N, Fua P, Lepetit V (2012) Gradient response
    • Intelligence 34(5):876-888 38. Hornung A, Wurm KM, Bennewitz M, Stachniss C, Burgard W (2013) Octomap: an efficient probabilis-
    • tic 3d mapping framework based on octrees. Auton Robot 34(3):189-206 39. Hu G, Huang S, Zhao L, Alempijevic A, Dissanayake G (2012) A robust rgb-d slam algorithm. In:
    • International Conference on Intelligent Robots and Systems, pp 1714-1719 40. Huynh O, Stanciulescu B (2015) Person re-identification using the silhouette shape described by a point
    • distribution model. In: IEEE Winter Conference on Applications of Computer Vision, pp 929-934 41. Janoch A, Karayev S, Jia Y, Barron JT, Fritz M, Saenko K, Darrell T (2013) A category-level 3d object
    • and Applications, pp 141-165 42. Jhuo IH, Gao S, Zhuang L, Lee D, Ma Y Unsupervised feature learning for rgb-d image classification 43. Jin L, Gao S, Li Z, Tang J (2014) Hand-crafted features or machine learnt features? together they improve
    • rgb-d object recognition. In: IEEE International Symposium on Multimedia, pp 311-319 44. Karpathy A, Miller S, Fei-Fei L (2013) Object discovery in 3d scenes via shape analysis. In: International
    • Conference on Robotics and Automation (ICRA), pp 2088-2095 45. Kerl C, Sturm J, Cremers D (2013) Robust odometry estimation for rgb-d cameras. In: International
    • Conference on Robotics and Automation, pp 3748-3754 46. Kepski M, Kwolek B (2014) Fall detection using ceiling-mounted 3d depth camera. In: International
    • Conference on Computer Vision Theory and Applications, vol 2, pp 640-647 47. Koppula HS, Gupta R, Saxena A (2013) Learning human activities and object affordances from rgb-d
    • videos. The International Journal of Robotics Research 32(8):951-970 48. Koppula HS, Anand A, Joachims T, Saxena A (2011) Semantic labeling of 3d point clouds for indoor
    • scenes. In: Advances in Neural Information Processing Systems, pp 244-252 49. Kumatani K, Arakawa T, Yamamoto K, McDonough J, Raj B, Singh R, Tashev I (2012) Microphone
    • Information Processing Association Conference:1-10 50. Kurakin A, Zhang Z, Liu Z (2012) A real time system for dynamic hand gesture recognition with a depth
    • sensor. In: European Signal Processing Conference (EUSIPCO), pp 1975-1979 51. Kwolek B, Kepski M (2014) Human fall detection on embedded platform using depth maps and wireless
    • accelerometer. Comput Methods Prog Biomed 117(3):489-501 52. Lai K, Bo L, Ren X, Fox D (2011) A large-scale hierarchical multi-view rgb-d object dataset. In:
    • International Conference on Robotics and Automation, pp 1817-1824 53. Lai K, Bo L, Ren X, Fox D (2013) Rgb-d object recognition: Features, algorithms, and a large scale
    • benchmark. In: Consumer Depth Cameras for Computer Vision, pp 167-192 54. Lee TK, Lim S, Lee S, An S, Oh SY (2012) Indoor mapping using planes extracted from noisy rgb-d
    • sensors. In: International Conference on Intelligent Robots and Systems, pp 1727-1733 55. Leibe B, Cornelis N, Cornelis K, Van Gool L (2007) Dynamic 3d scene analysis from a moving vehicle.
    • In: Conference on Computer Vision and Pattern Recognition, pp 1-8 56. Leroy J, Rocca F, Mancac¸s M, Gosselin B (2013) 3d head pose estimation for tv setups. In: Intelligent
    • Technologies for Interactive Entertainment, pp 55-64 57. Liu L, Shao L (2013) Learning discriminative representations from rgb-d video data. In: International
    • joint conference on Artificial Intelligence, pp 1493-1500 58. Liu K, Chen C, Jafari R, Kehtarnavaz N (2014) Fusion of inertial and depth sensor data for robust hand
    • gesture recognition. Sensors Journal 14(6):1898-1903 59. Luber M, Spinello L, Arras KO (2011) People tracking in rgb-d data with on-line boosted target models.
    • In: International Conference on Intelligent Robots and Systems, pp 3844-3849 60. Mason J, Marthi B, Parr R (2012) Object disappearance for object discovery. In: International Conference
    • on Intelligent Robots and Systems, pp 2836-2843 61. Mason J, Marthi B, Parr R (2014) Unsupervised discovery of object classes with a mobile robot. In:
    • International Conference on Robotics and Automation, pp 3074-3081 62. Mantecon T, del Bianco CR, Jaureguizar F, Garcia N (2014) Depth-based face recognition using local
    • quantized patterns adapted for range data. In: International Conference on Image Processing, pp 293-297 63. Meister S, Izadi S, Kohli P, Ha¨mmerle M, Rother C, Kondermann D (2012) When can we use
    • kinectfusion for ground truth acquisition? In: Workshop on color-depth camera fusion in robotics 64. Min R, Kose N, Dugelay JL (2014) Kinectfacedb: a kinect database for face recognition. IEEE
    • Transactions on Cybernetics 44(11):1534-1548 65. Nathan Silberman PK, Hoiem D, Fergus R (2012) Indoor segmentation and support inference from rgbd
    • images, in: European Conference on Computer Vision, pp 746-760 66. Narayan KS, Sha J, Singh A, Abbeel P Range sensor and silhouette fusion for high-quality 3d scanning,
    • sensors 32(33):26 67. Negin F, O¨ zdemir F, Akgu¨l CB, Yu¨ksel KA, Erc¸cil A (2013) A decision forest based feature selection
    • framework for action recognition from rgb-depth cameras. In: Image Analysis and Recognition, pp 648-
    • 657 68. Ni B, Wang G, Moulin P (2013) Rgbd-hudaact: A color-depth video database for human daily activity
    • recognition. In: Consumer Depth Cameras for Computer Vision, pp 193-208 69. Oikonomidis I, Kyriazis N, Argyros AA (2011) Efficient model-based 3d tracking of hand articulations
    • using kinect. In: British Machine Vision Conference, pp 1-11 70. Pomerleau F, Magnenat S, Colas F, Liu M, Siegwart R (2011) Tracking a depth camera: Parameter
    • exploration for fast icp. In: International Conference on Intelligent Robots and Systems, pp 3824-3829 71. Rekik A, Ben-Hamadou A, Mahdi W (2013) 3d face pose tracking using low quality depth cameras. In:
    • International Conference on Computer Vision Theory and Applications, vol 2, pp 223-228 72. Richtsfeld A, Morwald T, Prankl J, Zillich M, Vincze M (2012) Segmentation of unknown objects in
    • indoor environments. In: International Conference on Intelligent Robots and Systems, pp 4791-4796 73. Richtsfeld A, Mo¨rwald T, Prankl J, Zillich M, Vincze M (2014) Learning of perceptual grouping for
    • object segmentation on rgb-d data. Journal of visual communication and image representation 25(1):64-73 74. Rusu RB, Cousins S (2011) 3d is here: Point cloud library (pcl). In: International Conference on Robotics
    • and Automation, pp 1-4 75. Salas-Moreno RF, Glocken B, Kelly PH, Davison AJ (2014) Dense planar slam. In: IEEE International
    • Symposium on Mixed and Augmented Reality, pp 157-164 76. Satta R (2013) Dissimilarity-based people re-identification and search for intelligent video surveillance.
    • Ph.D. thesis 77. Shao T, Xu W, Zhou K, Wang J, Li D, Guo B (2012) An interactive approach to semantic modeling of
    • indoor scenes with an rgbd camera. ACM Trans Graph 31(6):136 78. Shotton J, Glocker B, Zach C, Izadi S, Criminisi A, Fitzgibbon A (2013) Scene coordinate regres-
    • Recognition, pp 2930-2937 79. Silberman L, Fergus R (2011) Indoor scene segmentation using a structured light sensor. In: International
    • Conference on Computer Vision - Workshop on 3D Representation and Recognition, pp 601-608 80. Singh A, Sha J, Narayan KS, Achim T, Abbeel P (2014) Bigbird: A large-scale 3d database of object
    • instances. In: International Conference on Robotics and Automation, pp 509-516 81. Song S, Xiao J (2013) Tracking revisited using rgbd camera: Unified benchmark and baselines. In:
    • International Conference on Computer Vision, pp 233-240 82. Song S, Lichtenberg SP, Xiao J (2015) Sun rgb-d: A rgb-d scene understanding benchmark suite. In:
    • IEEE Conference on Computer Vision and Pattern Recognition, pp 567-576 83. Spinello L, Arras KO (2011) People detection in rgb-d data. In: International Conference on Intelligent
    • Robots and Systems, pp 3838-3843 84. Sturm J, Magnenat S, Engelhard N, Pomerleau F, Colas F, Burgard W, Cremers D, Siegwart R (2011)
    • cameras at robotics: Science and systems conference, vol 2 85. Sturm J, Engelhard N, Endres F, Burgard W, Cremers D (2012) A benchmark for the evaluation of rgb-d
    • slam systems. In: International Conference on Intelligent Robot Systems, pp 573-580 86. Sturm J, Burgard W, Cremers D (2012) Evaluating egomotion and structure-from-motion approaches
    • on intelligent robot systems 87. Steinbruecker D, Sturm J, Cremers D (2011) Real-time visual odometry from dense rgb-d images. In:
    • Computer Vision, pp 719-722 88. Stein S, McKenna SJ (2013) Combining embedded accelerometers with computer vision for recognizing
    • pp 729-738 89. Stein S, Mckenna SJ (2013) User-adaptive models for recognizing food preparation activities. In:
    • International workshop on Multimedia for cooking & eating activities, pp 39-44 90. Sutton MA, Orteu JJ, Schreier H (2009) Image correlation for shape, motion and deformation
    • measurements: basic concepts theory and applications 91. Sun M, Bradski G, Xu BX, Savarese S (2010) Depth-encoded hough voting for joint object detection
    • and shape recovery. In: European Conference on Computer Vision, pp 658-671 92. Sung J, Ponce C, Selman B, Saxena A Human activity detection from rgbd images., plan, activity, and
    • intent recognition 64 93. Susanto W, Rohrbach M, Schiele B (2012) 3d object detection with multiple kinects. In: European
    • Conference on Computer Vision Workshops and Demonstrations, pp 93-102 94. Tao D, Jin L, Yang Z, Li X (2013) Rank preserving sparse learning for kinect based scene classification.
    • IEEE Transactions on Cybernetics 43(5):1406-1417 95. Tao D, Cheng J, Lin X, Yu J Local structure preserving discriminative projections for rgb-d sensor-based
    • scene classification, Information Sciences 96. Vaufreydaz D, Ne`gre A (2014) Mobilergbd, an open benchmark corpus for mobile rgb-d related
    • algorithms. In: International conference on control, Automation, Robotics and Vision 97. Wang J, Liu Z, Wu Y, Yuan J (2012) Mining actionlet ensemble for action recognition with depth
    • cameras. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 1290-1297 98. Wang J, Liu Z, Wu Y, Yuan J (2014) Learning actionlet ensemble for 3d human action recognition. IEEE
    • Transactions on Pattern Analysis and Machine Intelligence 36(5):914-927 99. Wohlkinger W, Aldoma A, Rusu RB, Vincze M (2012) 3dnet: Large-scale object class recognition from
    • cad models. In: International Conference on Robotics and Automation, pp 5384-5391 100. Wu D, Shao L (2014) Leveraging hierarchical parametric networks for skeletal joints based action
    • pp 724-731 101. Xiao J, Owens A, Torralba A (2013) Sun3d: A database of big spaces reconstructed using sfm and
    • object labels. In: International Conference on Computer Vision, pp 1625-1632 102. Yang Y, Guha A, Fernmueller C, Aloimonos Y (2014) Manipulation action tree bank: A knowledge
    • resource for humanoids:987-992 103. Yu G, Liu Z, Yuan J (2015) Discriminative orderlet mining for real-time recognition of human-object
    • interaction. In: Asian Conference on Computer Vision, pp 50-65 104. Zhang Q, Song X, Shao X, Shibasaki R, Zhao H (2013) Category modeling from just a single labeling:
    • Use depth information to guide the learning of 2d models. In: Conference on Computer Vision and
    • Pattern Recognition, pp 193-200 105. Zhou Q-Y, Koltun V (2013) Dense scene reconstruction with points of interest. ACM Trans Graph
  • No related research data.
  • No similar publications.
  • BioEntity Site Name
    GitHub

Share - Bookmark

Cite this article