中文摘要：

针对PMVS算法在多视倾斜影像密集匹配中的不足,结合城市三维建模的物方特点,将高程约束条件、聚类分析方法和候选影像排序策略引入其中,并用格网扩散代替原算法中的六方向扩散,从而形成了一种适合大倾斜影像的PMVS改进算法。实验结果表明：提出的改进算法能有效限制初始匹配的种子点个数,较大程度提高种子点的精度和质量,减小后续扩散和滤波的不确定性,使最终获得的点云个数增加78%,点云漏洞明显减少,甚至消失,为DEM生产和城市三维建模提供了一种新的技术手段。

英文摘要：

This paper proposes an improved patch-based multi-view stereo （PMVS）algorithm, aiming to over- come its limitations in the dense matching of multi-view oblique images. The new algorithm combines with the objects features in a three-dimensional modeling method for cities, makes use of elevation constrains, cluster analysis and candidate image sorting strategies, and it also uses a grid diffusion to substitute the original hexa- gonal-direction diffusion, and suits to large oblique images. An experiment shows that this improved PMVS can effectively restrict the seed number in initial matching, and greatly improve the accuracy and quality of seed points. The subsequent diffusion and filtering uncertainty are reduced. With this new algorithm, the final num- ber of seed points increases 78% , the point cloud vacuums decrease significantly, even totally vanish in some cases. It provides a new technology for DEM production and a three-dimensional modeling method for cities.