Structure-from-Motion (SfM) methods can be broadly

categorized as incremental or global according to their

ways to estimate initial camera poses. While incremental

system has advanced in robustness and accuracy, the ef-

ficiency remains its key challenge. To solve this problem,

global reconstruction system simultaneously estimates al-

l camera poses from the epipolar geometry graph, but it

is usually sensitive to outliers. In this work, we propose

a new hybrid SfM method to tackle the issues of efficien-

cy, accuracy and robustness in a unified framework. More

specifically, we propose an adaptive community-based ro-

tation averaging method first to estimate camera rotations

in a global manner. Then, based on these estimated camera

rotations, camera centers are computed in an incremental

way. Extensive experiments show that our hybrid method

performs similarly or better than many of the state-of-the-

art global SfM approaches, in terms of computational effi-

ciency, while achieves similar reconstruction accuracy and

robustness with two other state-of-the-art incremental SfM

approaches