A Block based 3D Map for Recognizing Three Dimensional Spaces

3차원 공간의 인식을 위한 블록기반 3D맵

  • Yi, Jong-Su (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Kim, Jun-Seong (School of Electrical and Electronics Engineering, Chung-Ang University)
  • 이정수 (중앙대학교 전자전기 공학부) ;
  • 김준성 (중앙대학교 전자전기 공학부)
  • Received : 2012.02.05
  • Accepted : 2012.06.21
  • Published : 2012.07.25


A 3D map provides useful information for intelligent services. Traditional 3D maps, however, consist of a raw image data and are not suitable for real-time applications. In this paper, we propose the Block-based 3D map, that represents three dimensional spaces in a collection of square blocks. The Block_based 3D map has two major variables: an object ratio and a block size. The object ratio is defined as the proportion of object pixels to space pixels in a block and determines the type of the block. The block size is defined as the number of pixels of the side of a block and determines the size of the block. Experiments show the advantage of the Block-based 3D map in reducing noise, and in saving the amount of processing data. With the block size of $40{\times}40$ and the object ratio of 30% to 50% we can get the most matched Block-based 3D map for the $320{\times}240$ depthmap. The Block-based 3D map provides useful information, that can produce a variety of new services with high added value in intelligent environments.


Supported by : 한국연구재단


  1. Yong Zhang; Yikang Gu; Vlatkovic, V.; Xiaojuan Wang; , "Progress of smart sensor and smart sensor networks," Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on, vol.4, pp. 3600-3606, June 2004.
  2. S. T. Barnard, M. A. Fischler, "Computational Stereo," ACM Computing Surveys, Volume 14, Issue 4, pp. 553-572, 1982. https://doi.org/10.1145/356893.356896
  3. C. F. Olson, "Maximum-likelihood image matching," Pattern Analysis and Machine Intelligence, IEEE Transactions on, Volume 24, No 6, pp. 853-857, 2002.
  4. 서자원, 김창익. "스테레오 카메라 영상처리 기술 및 동향," 전자공학회지, 제38권, 제2호, 31-36쪽, 2011년
  5. C. Huahua, X. Zezhong, "3D Map Building Based on Stereo Vision," Networking, Sensing and Control, ICNSC '06. Proceedings of the 2006 IEEE International Conference on, pp. 969-973, 2006.
  6. J. M. Saez, F. Escolano, "A global 3D map-building approach using stereo vision," Robotics and Automation, 2004. Proceedings. ICRA '04 IEEE International Conference on, Volume 2, pp. 1197-1202, 2004.
  7. A. Cappalunga, S. Cattani, A. Broggi, M. S. McDaniel, S. Dutta, "Real time 3D terrain elevation mapping using ants Optimization Algorithm and stereo vision," Intelligent Vehicles Symposium (IV), IEEE, pp. 902-909, 2010.
  8. S. Jian-Hong, J. Byung-Seung, L. Jong-Wook, L. Myo-taeg, "Stereo vision based 3D modeling system for mobile robot," Control Automation and Systems (ICCAS), International Conference on, pp. 71-75, 2010.
  9. KATS 기술보고서 제17호(지능형 홈 산업 및 표준화 동향), 기술표준원, 2010.
  10. 이정수, 김준성. "비전 시스템 구현을 위한 SAD 정합 알고리즘의 변형," 전자공학회논문지-CI, 제47권, 제5호, 61-66쪽, 2010년