立体影像的密集匹配一直是摄影测量和计算机视觉领域经久不衰的研究热点，其核心是快速且稳健可靠的逐像素匹配理论与方法。本项目创新性地提出基于代价矩阵和多重软约束的立体影像密集匹配理论与方法，从根本上改变了传统密集匹配算法的约束条件实现思路；率先提出结合基线长度、像平面法向量、交会角、空三匹配点等多重约束的最优立体像对自适应选择方法；提出基于全局能量函数最小化的物方多视点云融合方法；采用线缓冲区和赢家通吃思想实现线状地物影像边缘和深度图边缘的完全统一，进行无效匹配区域识别并联合面特征消除纹理贫乏区域的误匹配特征，提高密集匹配点云的精度水平；最终建立较为完整的多视影像多约束密集匹配理论和算法体系，实现卫星、航空、低空等传感器平台获取的多视立体影像的密集匹配，为高精度数字表面模型、真正射影像、建筑物三维重建等应用奠定坚实的理论和算法基础，同时为我国地理国情监测和智慧城市的发展提供技术支撑。

Dense matching with stereo images is one of the hot topics of Photogrammetry and Computer Vision communities in recent years. The key technologies are the theory and method of fast and robust pixel-by-pixel image matching. This proposal puts forward a novel dense matching theory and the corresponding realization methods of single stereo based on cost matrix and multiple soft constraints. This approach totally changes the traditional idea of applying constraint conditions during dense matching. A new idea of automatic optimal stereo pair selection is proposed by considering multiple conditions, such as baseline length, normal vector of image plane, intersection angle, and corresponding points of aerial triangulation. We also put forward a new method of multi-view point clouds fusion in the object space by minimizing the global energy function. Line buffer and winner takes all strategies are adopted to get complete identical edges between image and depth map of linear features. Invalid or mis-matching areas are recoginzed and area features from image segmentation are also integrated to eliminate mis-matches in texture less areas for further improving the accuracy of matched point clouds. Finally, we will establish new multi-view dense image matching theory and practical methods that can generate accurate point clouds with space, aerial, and low altitude images. Our achievements are promising to make good foundation on both theory and applications of digital surface model and true orthoimage generation, three-dimensional building model reconstruction, and to provide strong technical supports for geographical conditions monitoring and intellectual city applications.