A survey and technology trends of 3D features for object recognition

被引：0
作者：
Hashimoto M. [1 ]
Akizuki S. [1 ]
Takei S. [1 ]
机构：
[1] Graduate School of Computer and Cognitive Sciences, Chukyo University, 101-2, Yagoto-Honmachi, Showa-ku, Nagoya, Aichi
来源：
IEEJ Transactions on Electronics, Information and Systems | 2016年 / 136卷 / 08期
关键词：
3D features; Local Reference Frame; Object recognition; Robot vision;
D O I：
10.1541/ieejeiss.136.1038
中图分类号：
学科分类号：
摘要：
Recently, various kinds of 3D imaging devices such as laser range finder or time-of-flight sensor become popular, so a lot of practical algorithms for recognizing 3D objects using "point cloud data" have been proposed. In this paper, we will introduce typical object recognition approaches and survey various kinds of 3D features proposed by many researchers. As for some important techniques, their principle and characteristics are explained in detail. Also we will mention about the LRF (Local Reference Frame) which is very important factor to realize stable feature description and accurate pose estimation in practical use. © 2016 The Institute of Electrical Engineers of Japan.
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页码：1038 / 1046
页数：8
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共 67 条
  • [1] Besl P.J., McKay N.D., A method for registration of 3-D shapes, IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), 14, 2, pp. 239-256, (1992)
  • [2] Granger S., Pennec X., Multi-scale EM-ICP: A fast and robust approach for surface registration, European Conference on Computer Vision, 2353, pp. 418-432, (2002)
  • [3] Chetverikov D., Svirko D., Stepanov D., Krsek P., The trimmed iterative closest point algorithm, Proc. International Conference on Pattern Recognition, 3, pp. 545-548, (2002)
  • [4] ZinBer T., Schmidt J., Niemann H., A refind ICP algorithm for robust 3-D correspondence estimation, Proc. International Conference on Image Processing, 2-3, pp. II695-II708, (2003)
  • [5] Kaneko S., Kondo T., Miyamoto A., Robust Matching of 3D Contours Using Iterative Closest Point Algorithm Improved by M-estimation, Pattern Recognition, 36, 9, pp. 2041-2047, (2003)
  • [6] Fitzgibbon A.W., Robust Registration of 2D and 3D points sets, Image and Vision Computing, 21, pp. 1145-1153, (2003)
  • [7] Phillps J.M., Liu R., Tomasi C., Outlier robust ICP for minimizing fractional RMSD, International Conference on 3-D Digital Imaging and Modeling, pp. 427-434, (2007)
  • [8] Nuchter A., Lingemann K., Hertzberg J., Cached K-d tree search for ICP algorithms, International Conference on 3-D Digital Imaging and Modeling, pp. 419-426, (2007)
  • [9] Tateno K., Kotake D., Uchiyama S., A model fitting method using intensity and range images for bin-picking applications, IEICE Trans. Inf & Syst, J94-D, 8, pp. 1410-1422, (2011)
  • [10] Ikeuchi K., Kang S.B., Assembly Plan from Observation, AAAI Technical Report FS-93-04, pp. 115-119, (1993)
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