Learning a Depth Covariance Function
Fuente:
arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
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| _version_ | 1866917618655428608 |
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| author | Dexheimer, Eric Davison, Andrew J. |
| author_facet | Dexheimer, Eric Davison, Andrew J. |
| contents | We propose learning a depth covariance function with applications to geometric vision tasks. Given RGB images as input, the covariance function can be flexibly used to define priors over depth functions, predictive distributions given observations, and methods for active point selection. We leverage these techniques for a selection of downstream tasks: depth completion, bundle adjustment, and monocular dense visual odometry. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_12157 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Learning a Depth Covariance Function Dexheimer, Eric Davison, Andrew J. Computer Vision and Pattern Recognition Machine Learning Robotics We propose learning a depth covariance function with applications to geometric vision tasks. Given RGB images as input, the covariance function can be flexibly used to define priors over depth functions, predictive distributions given observations, and methods for active point selection. We leverage these techniques for a selection of downstream tasks: depth completion, bundle adjustment, and monocular dense visual odometry. |
| title | Learning a Depth Covariance Function |
| topic | Computer Vision and Pattern Recognition Machine Learning Robotics |
| url | https://arxiv.org/abs/2303.12157 |