【作者】 胡争；

【导师】 孙敏； 周晓光；

【作者基本信息】 中南大学 ， 大地测量学与测量工程， 2010， 硕士

【摘要】 三维数据的获取问题对于3DGIS而言具有极其重要的意义。基于摄影测量与多视几何理论的影像匹配技术可以获得地面高程信息、纹理数据等,它已逐渐成为三维数据获取的最主要手段之一。基于影像匹配所得的结果是稠密深度图,它包含了影像中每个象素的三维信息,但由此得到的三维数据用在3DGIS建模时需要进行大量简化,同时当影像中存在大范围非特征区域时,易产生大量的误匹配。为此,论文针对3DGIS建模的需求,研究了稀疏深度图匹配的关键技术,旨在快速自动生成特征区域内的深度图,回避对纹理稀疏区域的匹配。论文的主要工作有：1)介绍了双目立体视觉成像原理以及基于多视几何理论的核线影像求解方法,分析了影像匹配中的约束条件、难点和匹配算法的主要评价手段。2)提出了一种基于法向量的特征区域提取算法。该算法根据象素点邻域内法向量的变化程度来判断其是否为特征点,有效提取了灰度变化剧烈的区域,过滤了特征不明显的平滑区域。3)对现有的影像匹配算法进行了对比、分析和总结。提出一种基于特征区域的稀疏深度图匹配算法。将影像看成是一个连续的三维曲面,以特征区域作为匹配对象,利用匹配窗口中对应法向量的余弦值之总和构造匹配代价函数。基于特征区域的匹配可以回避对纹理缺乏区域的错误匹配,同时加快了计算速度。4)利用C++语言开发了上述算法,使用真实影像数据进行了匹配实验。结果表明,本文匹配算法可以有效地生成稀疏深度图,错误匹配率较低,并能满足3DGIS中的建模需求。更多还原

【Abstract】 The 3D data acquisition is very important for 3D Geographic Information System (3DGIS). Image matching based on Photogrammetry and multiple view geometry has become one of the main means of 3D data acquisition which can obtain ground elevation and texture data. Dense depth map as the result of image matching contains 3D information of each image pixel. But the 3D data extracted from this way need to be simplified if used for 3D modeling in 3DGIS. On the other hand, if sparse texture regions exist in the images, it’s difficult to overcome the error matches. Therefore, for the need of 3DGIS modeling, the author studies the key technologies of sparse depth map matching. The objective is to quickly generate sparse depth map automatically and avoid mismatching in texture-less regions.The main work is as follows:1) The imaging principle of binocular stereo vision, the basic theory and solution method of epipolar image generation are introduced. Then the image matching constraints, difficulties and evaluation means are analyzed.2) The author proposes an extraction algorithm of feature regions based on normal vector. The method judges whether the pixel is feature point or not according to the change degree of normal vectors in pixel neighborhood. It effectively extracts the regions in which the gray scale changes greatly and filters the smooth areas with no remarkable feature.3) After comparing and analyzing the matching algorithms in current reference, the author presents a sparse depth map matching algorithm based on feature regions. The algorithm takes one image as a continuous 3D surface, and mainly matches feature regions. The summary of cosine values of corresponding normal vectors in two match windows are adopted as the cost functions. The algorithm based on feature regions can filter sparse texture regions to avoid mismatch and save computer time.4) C++ language is used to implement this algorithm, and the real image data is used for checking it. The results show that the proposed algorithm is effective to obtain the sparse depth maps which can satisfy 3DGIS modeling.更多还原