Have a personal or library account? Click to login
Keypoint-Less, Heuristic Application of Local 3D Descriptors Cover

Keypoint-Less, Heuristic Application of Local 3D Descriptors

Foundations of Computing and Decision Sciences
Volume 42 (2017): Issue 3 (September 2017)
By: Bogdan Harasymowicz-Boggio and  Łukasz Chechliński  
Open Access
|Sep 2017

References

  1. [1] Heuros 3D object recognition system. https://bitbucket.org/rrgwut/heuros. Accessed: 04-05-2015.
    Search in Google ScholarBack to article
  2. [2] Aldoma, A., et al. CAD-model recognition and 6DOF pose estimation using 3D cues. In IEEE International Conference on Computer Vision Workshops, ICCV 2011 Workshops, Barcelona, Spain, November 6-13, 2011, 585–592. 2011.10.1109/ICCVW.2011.6130296
    Search in Google ScholarBack to article
  3. [3] Alexandre, L. A. 3D descriptors for object and category recognition: a comparative evaluation. In Workshop on Color-Depth Camera Fusion in Robotics at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Vilamoura, Portugal, 2012.
    Search in Google ScholarBack to article
  4. [4] Alexandre, L. A. Set distance functions for 3D object recognition. In 18th Iberoamerican Congress on Pattern Recognition, volume LNCS 8258 of Lecture Notes in Computer Science, 57–64. Springer, Havana, Cuba, 2013.10.1007/978-3-642-41822-8_8
    Search in Google ScholarBack to article
  5. [5] Cupec, R., et al. Detection of planar surfaces based on ransac and lad plane fitting. In Petrovic, I., Lilienthal, A. J., editors, ECMR, 37–42. KoREMA, 2009.
    Search in Google ScholarBack to article
  6. [6] Farid, R. Region-Growing Planar Segmentation for Robot Action Planning, 179–191. Springer International Publishing, Cham, 2015.10.1007/978-3-319-26350-2_16
    Search in Google ScholarBack to article
  7. [7] Filipe, S., Alexandre, L. A. A comparative evaluation of 3d keypoint detectors in a rgb-d object dataset. In 2014 International Conference on Computer Vision Theory and Applications (VISAPP). 2014.
    Search in Google ScholarBack to article
  8. [8] Filipe, S., Itti, L., Alexandre, L. A. BIK-BUS: Biologically motivated 3D keypoint based on bottom-up saliency. IEEE Transactions on Image Processing, 24(1):163–175, 2015.
    Search in Google ScholarBack to article
  9. [9] Ghorpade, V. K., et al. Performance evaluation of 3d keypoint detectors for time-of-fight depth data. In 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV), 1–6. 2016.10.1109/ICARCV.2016.7838686
    Search in Google ScholarBack to article
  10. [10] Harasymowicz-Boggio, B., Chechliński, L., Siemiatkowska, B. Nature-inspired, parallel object recognition. In Szewczyk, R., Zieliński, C., Kaliczyńska, M., editors, Progress in Automation, Robotics and Measuring Techniques. Control and Automation. Advances in Intelligent Systems and Computing vol. 350. Springer, 2015.10.1007/978-3-319-15796-2_6
    Search in Google ScholarBack to article
  11. [11] Harasymowicz-Boggio, B., Chechliński, u., Siemiatkowska, B. Significance of features in object recognition using depth sensors. Optica Applicata, 45(4):559–571, 2015.10.1007/978-3-319-02294-9_71
    Search in Google ScholarBack to article
  12. [12] Holz, D., et al. Robot soccer world cup xv. chapter Real-time Plane Segmentation Using RGB-D Cameras, 306–317. Springer-Verlag, Berlin, Heidelberg, 2012.10.1007/978-3-642-32060-6_26
    Search in Google ScholarBack to article
  13. [13] Izadi, S., et al. Kinectfusion: real-time 3D reconstruction and interaction using a moving depth camera. In Proceedings of the 24th annual ACM symposium on User interface software and technology, UIST ‘11, 559–568. ACM, New York, NY, USA, 2011.10.1145/2047196.2047270
    Search in Google ScholarBack to article
  14. [14] Kalogerakis, E., Hertzmann, A., Singh, K. Learning 3D mesh segmentation and labeling. ACM Trans. Graph., 29(4).10.1145/1778765.1778839
    Search in Google ScholarBack to article
  15. [15] Lai, K., Bo, L., Fox, D. Unsupervised feature learning for 3D scene labeling. In IEEE International Conference on on Robotics and Automation. 2014.10.1109/ICRA.2014.6907298
    Search in Google ScholarBack to article
  16. [16] Nathan Silberman, P. K., Derek Hoiem, Fergus, R. Indoor segmentation and support inference from RGBD images. In ECCV. 2012.10.1007/978-3-642-33715-4_54
    Search in Google ScholarBack to article
  17. [17] Neves, A. J. R., et al. Object detection based on plane segmentation and features matching for a service robot. International Journal of Computer, Electrical, Automation, Control and Information Engineering, 10(4):745 – 752, 2016.
    Search in Google ScholarBack to article
  18. [18] Osada, R., et al. Shape distributions. ACM Transactions on Graphics, 21(4):807–832, 2002.10.1145/571647.571648
    Search in Google ScholarBack to article
  19. [19] Quigley, M., et al. Ros: an open-source robot operating system. In ICRA Workshop on Open Source Software. 2009.
    Search in Google ScholarBack to article
  20. [20] Ren, X., Bo, L., Fox, D. RGB-D scene labeling: Features and algorithms. In IEEE International Conference on Computer Vision and Pattern Recognition, 2759–2766. 2012.
    Search in Google ScholarBack to article
  21. [21] Rusu, R. B., Blodow, N., Beetz, M. Fast point feature histograms (FPFH) for 3D registration. In in In Proceedings of the International Conference on Robotics and Automation (ICRA. 2009.10.1109/ROBOT.2009.5152473
    Search in Google ScholarBack to article
  22. [22] Rusu, R. B., Cousins, S. 3d is here: Point cloud library (pcl). In International Conference on Robotics and Automation. Shanghai, China, 2011.10.1109/ICRA.2011.5980567
    Search in Google ScholarBack to article
  23. [23] Rusu, R. B., et al. Learning Informative Point Classes for the Acquisition of Object Model Maps. In Proceedings of the 10th International Conference on Control, Automation, Robotics and Vision (ICARCV), Hanoi, Vietnam, December 17-20. 2008.10.1109/ICARCV.2008.4795593
    Search in Google ScholarBack to article
  24. [24] Rusu, R. B., et al. Close-range scene segmentation and reconstruction of 3d point cloud maps for mobile manipulation in domestic environments. In 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, 1–6. 2009.10.1109/IROS.2009.5354683
    Search in Google ScholarBack to article
  25. [25] Rusu, R. B., et al. Detecting and segmenting objects for mobile manipulation. In Proceedings of IEEE Workshop on Search in 3D and Video (S3DV), held in conjunction with the 12th IEEE International Conference on Computer Vision (ICCV). Kyoto, Japan, 2009.
    Search in Google ScholarBack to article
  26. [26] Rusu, R. B., et al. Fast 3d recognition and pose using the viewpoint feature histogram. In Proceedings of the 23rd IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Taipei, Taiwan, 2010.10.1109/IROS.2010.5651280
    Search in Google ScholarBack to article
  27. [27] Song, S., Xiao, J. Deep sliding shapes for amodal 3d object detection in RGB-D images. CoRR, abs/1511.02300, 2015.
    Search in Google ScholarBack to article
DOI: https://doi.org/10.1515/fcds-2017-0012 | Journal eISSN: 2300-3405 | Journal ISSN: 0867-6356
Journal RSS Feed
Language: English
Page range: 239 - 255
Published on: Sep 9, 2017
Published by: Poznan University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
RGBD,
object detection,
3D descriptors,
keypointless
Related subjects:
Computer sciences,
Artificial intelligence,
Software development

© 2017 Bogdan Harasymowicz-Boggio, Łukasz Chechliński, published by Poznan University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Previous articleVolume 42 (2017): Issue 3 (September 2017)Next article