The incremental Structure-from-Motion (SfM) technique

has advanced in both robustness and accuracy, but the effi-

ciency and scalability remain its key challenges. In this pa-

per, we propose a novel batched incremental SfM technique

to tackle these problems in a unified framework, where two

iteration loops are contained. The inner loop is a tracks tri-

angulation loop, where a novel tracks selection method is

proposed to find a compact subset of tracks for the bundle

adjustment (BA). The outer loop is a camera registration

loop, where a batch of cameras are simultaneously added

to alleviate the drifting risk and reduce the running times

of BA. By the tracks selection and batched camera registra-

tion, we find these two iteration loops converge fast. Ex-

tensive experiments demonstrate that our new SfM system

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

art SfM systems in terms of camera calibration accuracy,

while is more efficient, robust and scalable for large-scale

scene reconstruction.