  Accuracy and efficiency are two key problems in large scale incremental
Structure from Motion (SfM). In this paper, we propose a unified framework to
divide the image set into clusters suitable for reconstruction as well as find
multiple reliable and stable starting points. Image partitioning performs in
two steps. First, some small image groups are selected at places with high
image density, and then all the images are clustered according to their optimal
reconstruction paths to these image groups. This promises that the scene is
always reconstructed from dense places to sparse areas, which can reduce error
accumulation when images have weak overlap. To enable faster speed, images
outside the selected group in each cluster are further divided to achieve a
greater degree of parallelism. Experiments show that our method achieves
significant speedup, higher accuracy and better completeness.
