  Structure-from-Motion approaches could be broadly divided into two classes:
incremental and global. While incremental manner is robust to outliers, it
suffers from error accumulation and heavy computation load. The global manner
has the advantage of simultaneously estimating all camera poses, but it is
usually sensitive to epipolar geometry outliers. In this paper, we propose an
adaptive community-based SfM (CSfM) method which takes both robustness and
efficiency into consideration. First, the epipolar geometry graph is
partitioned into separate communities. Then, the reconstruction problem is
solved for each community in parallel. Finally, the reconstruction results are
merged by a novel global similarity averaging method, which solves three convex
$L1$ optimization problems. Experimental results show that our method performs
better than many of the state-of-the-art global SfM approaches in terms of
computational efficiency, while achieves similar or better reconstruction
accuracy and robustness than many of the state-of-the-art incremental SfM
approaches.
