Abstract
We propose a strategy for improving camera location estimation in structure from motion. Our setting assumes highly corrupted pairwise directions (i.e., normalized relative location vectors), so there is a clear room for improving current state-of-the-art solutions for this problem. Our strategy identifies severely corrupted pairwise directions by using a geometric consistency condition. It then selects a cleaner set of pairwise directions as a preprocessing step for common solvers. We theoretically guarantee the successful performance of a basic version of our strategy under a synthetic corruption model. Numerical results on artificial and real data demonstrate the significant improvement obtained by our strategy.
Original language | English (US) |
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Title of host publication | Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018 |
Publisher | IEEE Computer Society |
Pages | 2868-2876 |
Number of pages | 9 |
ISBN (Electronic) | 9781538664209 |
DOIs | |
State | Published - Dec 14 2018 |
Event | 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018 - Salt Lake City, United States Duration: Jun 18 2018 → Jun 22 2018 |
Publication series
Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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ISSN (Print) | 1063-6919 |
Conference
Conference | 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018 |
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Country/Territory | United States |
City | Salt Lake City |
Period | 6/18/18 → 6/22/18 |
Bibliographical note
Funding Information:This work was supported by NSF award DMS-14-18386.