Learning a Depth Covariance Function

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Mar 21, 2023
Eric Dexheimer, Andrew J. Davison
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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.

* CVPR 2023. Project page: https://edexheim.github.io/depth_cov/  
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