LiFCal: Online Light Field Camera Calibration via Bundle Adjustment
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929468072787968 |
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| author | Fleith, Aymeric Ahmed, Doaa Cremers, Daniel Zeller, Niclas |
| author_facet | Fleith, Aymeric Ahmed, Doaa Cremers, Daniel Zeller, Niclas |
| contents | We propose LiFCal, a novel geometric online calibration pipeline for MLA-based light field cameras. LiFCal accurately determines model parameters from a moving camera sequence without precise calibration targets, integrating arbitrary metric scaling constraints. It optimizes intrinsic parameters of the light field camera model, the 3D coordinates of a sparse set of scene points and camera poses in a single bundle adjustment defined directly on micro image points.
We show that LiFCal can reliably and repeatably calibrate a focused plenoptic camera using different input sequences, providing intrinsic camera parameters extremely close to state-of-the-art methods, while offering two main advantages: it can be applied in a target-free scene, and it is implemented online in a complete and continuous pipeline.
Furthermore, we demonstrate the quality of the obtained camera parameters in downstream tasks like depth estimation and SLAM.
Webpage: https://lifcal.github.io/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_11682 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LiFCal: Online Light Field Camera Calibration via Bundle Adjustment Fleith, Aymeric Ahmed, Doaa Cremers, Daniel Zeller, Niclas Image and Video Processing Computer Vision and Pattern Recognition We propose LiFCal, a novel geometric online calibration pipeline for MLA-based light field cameras. LiFCal accurately determines model parameters from a moving camera sequence without precise calibration targets, integrating arbitrary metric scaling constraints. It optimizes intrinsic parameters of the light field camera model, the 3D coordinates of a sparse set of scene points and camera poses in a single bundle adjustment defined directly on micro image points. We show that LiFCal can reliably and repeatably calibrate a focused plenoptic camera using different input sequences, providing intrinsic camera parameters extremely close to state-of-the-art methods, while offering two main advantages: it can be applied in a target-free scene, and it is implemented online in a complete and continuous pipeline. Furthermore, we demonstrate the quality of the obtained camera parameters in downstream tasks like depth estimation and SLAM. Webpage: https://lifcal.github.io/ |
| title | LiFCal: Online Light Field Camera Calibration via Bundle Adjustment |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.11682 |