AIRR  >> Vol. 3 No. 4 (November 2014)

    采用彩色分割立体匹配与简化点云的三维目标快速重建
    Fast Reconstruction of 3-D Object Based on Color Image Segmentation Stereo Matching and Point Cloud Reduction

  • 全文下载: PDF(792KB) HTML    PP.55-61   DOI: 10.12677/AIRR.2014.34009  
  • 下载量: 967  浏览量: 3,174   科研立项经费支持

作者:  

李鹤喜,张娟娟,孙玲云:五邑大学计算机学院,江门

关键词:
均值漂移图像分割立体匹配点云三维重建Mean Shift Image Segmentation Stereo Matching Point Cloud 3D Reconstruction

摘要:

本文将彩色分割立体匹配和简化点云技术用于三维目标的快速重建。对于采集的左右两幅三维目标图像,首先采用均值漂移算法进行彩色图像分割,然后按区域匹配算法进行快速立体匹配,获得初始视差,再应用置信传播法进行全局视差优化,从而得到精确的视差图与空间点云。应用空间表面曲率准则对获取的密集点云进行简化,并采用Delaunay三角剖分算法进行三维重建。实验结果表明:采用彩色图像分割与置信传播相结合,能够改善立体匹配效率并保证了匹配质量;基于曲率的点云简化,既提高了三维重建速度也得到了满意的重建效果。

Color image segmentation stereo matching and point cloud reduction method is used for fast re-construction of 3-dimensional (3-D) object in this paper. For two captured images of a 3-D object, color image segmentation is first carried out using mean shift algorithm and initial disparity is computed using fast region-based stereo matching, and then the accurate disparity and point cloud of the 3-D object are obtained using belief propagation method to optimize global disparity. The 3-D object is reconstructed using Delaunay triangulation algorithm and point cloud reduction processing based on a surface curvature criterion. The experimental results show that the combi-nation of color image segmentation with belief propagation method can improve stereo matching efficiency and ensure matching quality, and the point cloud reduction technique can rise 3D re-construction speed and obtain satisfactory 3-D reconstruction result.

文章引用:
李鹤喜, 张娟娟, 孙玲云. 采用彩色分割立体匹配与简化点云的三维目标快速重建[J]. 人工智能与机器人研究, 2014, 3(4): 55-61. http://dx.doi.org/10.12677/AIRR.2014.34009

参考文献

[1] Kolmogorov, V. and Zabih, R. (2001) Computing visual correspondence with occlusions using graph cuts. Eighth In-ternational Conference on Computer Vision, 2, 508-515.
[2] Felzenszwalb, P.F. and Zabih, R. (2011) Dynamic pro-gramming and graph cut algorithms in computer vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 721-740.
[3] Yang, Q., Wang, L., Yang, R., el al. (2008) Stereo matching with color-weighted correlation, hierarchical belief propagation, and occlusion handling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31, 492-504.
[4] Klaus, A., Sormann, M. and Karner, K. (2006) Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure. 18th International Conference on Pattern Recognition, 3, 15-18.
[5] Deng, Y. and Lin, X. (2006) A fast line segment based dense stereo algorithm using tree dynamic pro-gramming. Proceedings of 9th European Conference on Computer Vision, Graz, 7-13 May 2006, 201-212.
[6] Comaniciu, D. and Meer, P. (1999) Mean shift analysis and applications. International Conference on Computer Vision, 2, 1197-1203.