Robust and Accurate Hybrid Structure-From-Moti

In this paper, we propose a hybrid Structure-from-Motion scheme which combines the strength of both global and local incremental SfM methods to get a drift-free and accurate estimation with lower time consumption. More specifically, we propose to construct a robust maximum leaf spanning tree (RMLST)...

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Bibliographic Details
Published in2019 IEEE International Conference on Image Processing (ICIP) pp. 494 - 498
Main Authors Li, Rui, Gong, Dong, Sun, Jinqiu, Zhu, Yu, Wei, Ziwei, Zhang, Yanning
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2019
Subjects
Bundle adjustment
Cameras
community-based local sfm
Computer science
Estimation
hybrid structure-from-motion
Image edge detection
Image reconstruction
loop consistency check
robust maximum spanning tree
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Summary:In this paper, we propose a hybrid Structure-from-Motion scheme which combines the strength of both global and local incremental SfM methods to get a drift-free and accurate estimation with lower time consumption. More specifically, we propose to construct a robust maximum leaf spanning tree (RMLST) from the initial scene graph and further expand it to a robust graph (RG) to grasp the global picture of camera distribution and scene structure. Then the views in the robust graph are solved in global manner as an initial estimation. After that, the remaining views are estimated with the proposed community-based local incremental approach to guarantee local accuracy and scalability. Bundle adjustment is conducted to optimize the estimation. Experiments show that our method is robust and free from the scene drift as global SfM, and shows much better efficiency than incremental approaches. Besides, our algorithm achieves higher accuracy compared with the state-of-the-art methods.
ISSN:2381-8549
DOI:10.1109/ICIP.2019.8803814