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Global Structure-from-Motion (SfM) techniques have demonstrated superior efficiency and accuracy than the conventional incremental approach in many recent studies. This work proposes a divide-and-conquer framework to solve very large global SfM at the scale of millions of images. Specifically, we first divide all images into multiple partitions that preserve strong data association for wellposed and parallel local motion averaging. Then, we solve a global motion averaging that determinesdoi:10.1109/cvpr.2018.00480 dblp:conf/cvpr/ZhuZZSFTQ18 fatcat:i3nxpgjpkvdwxcack7qcm5ymg4