Abstract
To the best of increasing robotic vision in 3D conceptual for recognizing this living world, this paper proposed a 3D recognition system by combining the local feature and global verification technique. To approach this, we modified the state-of-art methods and organized it as a robust hybrid flow. Another contribution to this paper, we release the finest parameters to the Kinect sensor as well as the dataset. In the proposed framework, we expect the pre-process can deal with range filtering, noise reduction, and point cloud refinement. After this, the captured point cloud is more reliable and better to describe the object surface. The Second part is focused on recognition and pose estimation. We here refer two robust methods, SHOT descriptor and Hough Voting, one for the local feature generation and the other contributes to the object alignment. Finally, through the ICP to refine the pose matrix, we remove the false positive while verifying the good instance. Moreover, we design a keypoint selective mechanism after the hypothesis verification stage back into local conception.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Lowe, D.G.: Local feature view clustering for 3D object recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Kauai, HI, USA, 8–14 December 2001, pp. 682–688 (2001)
Ponce, J., Lazebnik, S., Rothganger, F., Schmid, C.: Towards true 3D object recognition. In: International Conference on Computer Vision and Pattern Recognition (CVPR), Washington, pp. 4034–4041 (2004)
Toshev, A., Makadia, A., Daniilidis, K.: Shape-based object recognition in videos using 3D synthetic object models. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Miami, Florida, USA, 20–25 June 2009, pp. 288–295 (2009)
Hetzel, G., Leibe, B., Levi, P., Schiele, B.: 3D object recognition from range images using local feature histograms. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Kauai, HI, USA, 8–14 December 2001, pp. 294–299 (2001)
Gomes, R.B., da Silva, B.M.F., de MedeirosRocha, L.K., Aroca, R.V., Velho, L.C.P.R., Gonçalves, L.M.G.: Efficient 3D object recognition using foveated point clouds. Comput. Graph. 37, 496–508 (2013)
Hsiao, E., Collet, A., Hebert, M.: Making specific features less discriminative to improve point-based 3D object recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA, 13–18 June 2010, pp. 2653–2660 (2010)
Drost, B., Ulrich, M., Navab, N., Ilic, S.: Model globally, match locally: efficient and robust 3D object recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA, 13–18 June 2010, pp. 998–1005 (2010)
Tombari, F., di Stefano, L.: Object recognition in 3D scenes with occlusions and clutter by Hough voting. In: Fourth Pacific-Rim Symposium on Image and Video Technology (PSIVT), pp. 349–355 (2010)
Aldoma, A., Marton, Z., Tombari, F., Wohlkinger, W., Potthast, C., Zeisl, B., Rusu, R.B., Gedikli, S., Vincze, M.: Tutorial: point cloud library: three-dimensional object recognition and 6 DOF pose estimation. IEEE Robot. Automat. Mag. 19, 80–91 (2012)
Muja, M., Lowe, D.G.: Fast approximate nearest neighbors with automatic algorithm configuration. In: International Conference on Computer Vision Theory and Application (VISSAPP), Lisboa, Portugal, 5–8 February 2009, pp. 331–340 (2009)
Hoppe, H., DeRose, T., Duchamp, T., McDonald, J.A., Stuetzle, W.: Surface reconstruction from unorganized points. In: Proceedings of the 19th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH, Chicago, IL, USA, 27–31 July 1992, pp. 71–78 (1992)
Mitra, N.J., Nguyen, A., Guibas, L.J.: Estimating surface normals in noisy point cloud data. Int. J. Comput. Geom. Appl. 14, 261–276 (2004)
Tombari, F., Salti, S., Stefano, L.: Unique signatures of histograms for local surface description. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6313, pp. 356–369. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15558-1_26
Novatnack, J., Nishino, K.: Scale-dependent/invariant local 3D shape descriptors for fully automatic registration of multiple sets of range images. In: European Conference on Computer Vision, Marseille, France, 12–18 October 2008, pp. 440–453 (2008)
Mian, A.S., Bennamoun, M., Owens, R.A.: On the repeatability and quality of keypoints for local feature-based 3D object retrieval from cluttered scenes. Int. J. Comput. Vis. (IJCV) 89, 348–361 (2010)
Darom, T., Keller, Y.: Scale-invariant features for 3-D mesh models. IEEE Trans. Image Process. 21, 2758–2769 (2012)
Knopp, J., Prasad, M., Willems, G., Timofte, R., Gool, L.J.V.: Hough transform and 3D SURF for robust three dimensional classification. In: 11th European Conference on Computer Vision, Heraklion, Crete, Greece, 5–11 September 2010, pp. 589–602 (2010)
Johnson, A.E., Hebert, M.: Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 21, 433–449 (1999)
Dorai, C., Jain, A.K.: COSMOS - a representation scheme for 3D free-form objects. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 19, 1115–1130 (1997)
Chen, H., Bhanu, B.: 3D free-form object recognition in range images using local surface patches. Pattern Recogn. Lett. 28, 1252–1262 (2007)
Rusu, R.B., Blodow, N., Marton, Z.C., Beetz, M.: Aligning point cloud views using persistent feature histograms. In: International Conference on Intelligent Robots and Systems, 22–26 September 2008, pp. 3384–3391 (2008)
Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (FPFH) for 3D registration. In: IEEE International Conference on Robotics and Automation (ICRA), Kobe, Japan, 12–17 May 2009, pp. 3212–3217 (2009)
Zaharescu, A., Boyer, E., Horaud, R.: Keypoints and local descriptors of scalar functions on 2D manifolds. Int. J. Comput. Vis. (IJCV) 100, 78–98 (2012)
Aldoma, A., Tombari, F., Stefano, L., Vincze, M.: A global hypotheses verification method for 3D object recognition. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7574, pp. 511–524. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33712-3_37
PointClouds.org: Point cloud library (2014). http://pointclouds.org/
Acknowledgement
The support of this work in part by the Ministry of Science and Technology of Taiwan under Grant MOST 104-2221-E-194-058-MY2 is gratefully acknowledged.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Cheng, K.S., Lin, H.Y., Van Luan, T. (2017). A 3D Recognition System with Local-Global Collaboration. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10117. Springer, Cham. https://doi.org/10.1007/978-3-319-54427-4_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-54427-4_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-54426-7
Online ISBN: 978-3-319-54427-4
eBook Packages: Computer ScienceComputer Science (R0)