Deep fundamental matrix estimation without correspondences
Research output: Contribution to journal › Conference article › Research › peer-review
Estimating fundamental matrices is a classic problem in computer vision. Traditional methods rely heavily on the correctness of estimated key-point correspondences, which can be noisy and unreliable. As a result, it is difficult for these methods to handle image pairs with large occlusion or significantly different camera poses. In this paper, we propose novel neural network architectures to estimate fundamental matrices in an end-to-end manner without relying on point correspondences. New modules and layers are introduced in order to preserve mathematical properties of the fundamental matrix as a homogeneous rank-2 matrix with seven degrees of freedom. We analyze performance of the proposed models using various metrics on the KITTI dataset, and show that they achieve competitive performance with traditional methods without the need for extracting correspondences.
Original language | English |
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Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Pages (from-to) | 485-497 |
Number of pages | 13 |
ISSN | 0302-9743 |
DOIs | |
Publication status | Published - 2019 |
Externally published | Yes |
Event | 15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany Duration: 8 Sep 2018 → 14 Sep 2018 |
Conference
Conference | 15th European Conference on Computer Vision, ECCV 2018 |
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Country | Germany |
City | Munich |
Period | 08/09/2018 → 14/09/2018 |
Bibliographical note
Publisher Copyright:
© Springer Nature Switzerland AG 2019.
- Deep learning, Epipolar geometry, Fundamental matrix, Stereo
Research areas
ID: 301824797