Fast Reconstruction of 3-D Object Based on Color Image Segmentation Stereo Matching and Point Cloud Reduction
Abstract: 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 (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。 人工智能与机器人研究， 3， 55-61. doi: 10.12677/AIRR.2014.34009
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