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2023 CVPR CVPR 2023

Learning a Depth Covariance Function

Abstract

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.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Computer Vision and Machine Learning
🧭 Keyword Pioneer — depth covariance function
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics

Authors

Eric Dexheimer , Andrew J. Davison

Topics

Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling Machine Learning > Core Methods > Representation Learning Machine Learning > Optimization & Theory > Bayesian Inference Machine Learning > Bayesian & Probabilistic > Gaussian Processes Computer Vision > Domain-Specific > Robotics Computer Vision > Processing > Depth Estimation

Keywords

visual odometry bayesian prior depth completion bundle adjustment geometric vision depth covariance function monocular dense visual odometry predictive depth distribution active point selection
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