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The processing time of incremental structure from motion increases exponentially with the number of images. As a result, a huge amount of time is needed for large datasets. In this paper, to improve time efficiency, a block partitioning and a merging strategy are proposed. We automatically split the image set into several overlapping subsets, and then each subset can be processed in parallel. Finally, the reconstruction results of each subset can be merged together according to the shared images and tie points. The image adjacency matrix obtained from the feature matching result is the input of our block partitioning algorithm. And by repeatedly using the matrix bandwidth reduction algorithm to reorder the images, the block can be partitioned into subsets. The partitioning result is satisfactory, namely, images assigned into a subset have a very strong connection, and the shape of each subset is compact. Most importantly, the algorithm is very simple and fast. We have successfully processed many large-scale aerial image datasets in a computer cluster system with 10 processing nodes. And, the time efficiency and the precision of the reconstruction are satisfactory.

Block Partitioning and Merging for Processing Large-Scale Structure From Motion Problems in Distributed Manner