Voting-based Incremental Structure-from-Motion

Hainan Cui, Shuhan Shen, Wei Gao

Abstract: Incremental Structure-from-Motion (SfM) technique is the most prevalent way for image-based reconstruction, but its robustness is highly relying on each camera registration, where a false calibration could make everything following fail. In this paper, we propose a voting-based incremental SfM approach to improve upon the camera registration process. First, the degree of closeness between cameras is used as the vote to determine which cameras are going to register. Then, for each camera, two methods are simultaneously used to estimate the camera pose, and the number of inliers is used as the vote to determine which pose is more accurate. Finally, by estimating the priori global camera rotations from the view-graph, the camera poses that are consistent with the priori camera rotations are considered as getting double votes and preferentially kept. After all these prioritized cameras are calibrated, the other cameras are then incrementally registered. Compared to the state-of-the-art incremental SfM approaches, extensive experiments demonstrate that our system performs similarly or better in terms of reconstruction efficiency, while achieves a better robustness and accuracy. Especially for the ambiguous datasets, our system has a better potential to reconstruct them.