期刊
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
卷 156, 期 -, 页码 202-214出版社
ELSEVIER
DOI: 10.1016/j.isprsjprs.2019.08.005
关键词
Image-based modeling; Structure-from-motion; Track selection; Rotation averaging; Camera prioritization
类别
资金
- National Key R&D Program of China [2016YFB0502002]
- Natural Science Foundation of China [61703397, 61632003]
- Henan Science and Technology Innovation Outstanding Youth Program [184100510009]
- Henan University Scientific and Technological Innovation Team Support Program [19IRTSTHN012]
Incremental Structure-from-Motion (SfM) techniques have exhibited superior practicability in many recent studies; however, efficiency and robustness remain key challenges for these techniques. In this work, we propose a new incremental SfM method that overcomes these problems in a united framework that contains two iteration loops. The inner loop is a track selection loop, where a well-conditioned subset of the feature tracks is iteratively selected to accelerate the time-consuming bundle adjustment. The outer loop is a camera registration loop, where the a priori camera rotations are estimated via rotation averaging on multiple orthogonal maximum spanning trees (OMSTs) of the view-graph and used as weak supervision for the registration. The calibrated camera poses that agree with the a priori camera rotations are preferentially registered, and after all the consistent cameras have been calibrated, the remaining cameras are incrementally registered. The results of extensive experiments demonstrate that our system can reconstruct both general and ambiguous image datasets, and our system outperforms many state-of-the-art SfM systems in terms of efficiency and robustness.
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