【摘要】 针对并行式运动恢复结构(SfM)在局部场景合并时稳健性差的问题，提出一种摄影测量局部场景稳健合并的并行式SfM方法。对整个场景的影像关联图进行分块及扩展处理，得到相互重叠的子区块，并利用一种改进的增量式SfM方法生成局部场景重建结果。在局部场景合并时，首先利用局部场景的重叠关系构建子区块关联图，并以子区块三元组为单元，进行粗差剔除；然后，利用子区块三元组的代数性质，优化得到更符合几何一致性的子区块间的相对变换；最后，从上述结果中计算得到更准确的局部场景到统一坐标系下的尺度、旋转、平移变换。试验采用无人机影像，结果表明本文方法在局部场景合并时有更好的稳健性，而且SfM结果的精确度也要优于其他并行式方法和COLMAP,在摄影测量和实景三维重建中有较大的应用潜力。更多还原

【Abstract】 In this paper, we proposed an improved parallel structure from motion pipeline in photogrammetry by robustifying the merging of subblock reconstructions in a better fashion. Specifically, the whole block represented by a view graph is divided into a number of overlapped subblocks via graph partition and expansion, and an improved incremental SfM is employed to generate the SfM reconstruction of each subblock. To merge these subblock SfM reconstructions in a more robust manner, a subblock graph indicating the overlapping relationship of subblock reconstructions is first built. By considering the geometry consistencies of subblock triplets, gross errors are detected. Then, we leverage the algebraic properties of subblock triplets, which aims to make them more geometrically consistent, to refine the relative transformations between subblock reconstructions. Finally, more accurate relative transformations between subblock reconstructions can be obtained to boost the subsequential merging. Experimental results using UAV images show that the proposed method can guarantee robustness in the subblock reconstruction merging stage. The precision of our SfM results is better than several state-of-the-art parallel SfM methods and the popular COLMAP. Furthermore, it has significant potential for use in photogrammetry and 3D Real Scene reconstruction.更多还原