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一种快速的三维扫描数据自动配准方法
引用本文:杨 棽,齐 越,沈旭昆,赵沁平.一种快速的三维扫描数据自动配准方法[J].软件学报,2010,21(6):1438-1450.
作者姓名:杨 棽  齐 越  沈旭昆  赵沁平
作者单位:北京航空航天大学,虚拟现实技术与系统国家重点实验室,北京,100191
基金项目:Supported by the National Natural Science Foundation of China under Grant Nos.60533070, 60773153 (国家自然科学基金); the Key Grant Project of the Ministry of Education of China under Grant No.308004 (国家教育部科学技术研究重大项目); the National High-Tech Research and Development Plan of China under Grant No.2009AA012102 (国家高技术研究发展计划(863)); the Beijing Municipal Natural Science Foundation of China under Grant No.4102037 (北京市自然科学基金)
摘    要:研究了两幅和多幅深度图像的自动配准问题.在配准两幅深度图像时,结合二维纹理图像配准深度图像,具体过程是:首先,从扫描数据中提取纹理图像,特别地,针对不包含纹理图像的扫描数据提出了一种根据深度图像直接生成纹理图像的方法;然后,基于SIFT(scale-invariant feature transform)特征提取纹理图像中的兴趣像素,并通过预过滤和交叉检验兴趣像素等方法从中找出匹配像素对的候选集;之后,使用RANSAC(random sample consensus)算法,根据三维几何信息的约束找出候选集中正确的匹配像素对和相对应的匹配顶点对,并根据这些匹配顶点对计算出两幅深度图像间的刚体置换矩阵;最后,使用改进的ICP(iterative closest point)算法优化这一结果.在配准多幅深度图像时,提出了一种快速构建模型图的方法,可以避免对任意两幅深度图像作配准,提高了配准速度.该方法已成功应用于多种文物的三维逼真建模.

关 键 词:三维扫描数据  深度图像  纹理图像  粗略配准  多幅图像配准  图像配准
修稿时间:2008/12/10 0:00:00

Rapid and Automatic Method for 3D Scanned Data Registration
YANG Shen,QI Yue,SHEN Xu-Kun and ZHAO Qin-Ping.Rapid and Automatic Method for 3D Scanned Data Registration[J].Journal of Software,2010,21(6):1438-1450.
Authors:YANG Shen  QI Yue  SHEN Xu-Kun and ZHAO Qin-Ping
Abstract:This paper presents a rapid method to align large sets of 3D scanned data automatically. The method incorporates the technique of image registration into the pair-wise registration. Firstly, it retrieves two texture images from the scanned data to align. A method is proposed to generate the texture image from the range image when scanned data do not contain the texture information. Secondly, it detects the features using SIFT (scale-invariant feature transform) on texture images, and a set of potential corresponding pixels is selected by means of pre-filter and cross validation. Then a matching algorithm, based on RANSAC (random sample consensus) algorithm, is applied to specify the matching pixel pairs between two images. All matches obtained are mapped to 3D space and used to estimate the rigid transformation. Finally, a modified ICP (iterative closest point) algorithm is applied to refine the result. The paper also presents a method to create model graph rapidly for multi-view registration which avoids aligning all pairs of range images. This reconstruction technique achieves a robust and high performance in the application of automatic rebuilding 3D models of culture heritages.
Keywords:3D scanned data  range image  texture image  coarse registration  multi-view registration  image  registration
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