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Found 4 papers, 1 papers with code
Does it work outside this benchmark? Introducing the Rigid Depth Constructor tool, depth validation dataset construction in rigid scenes for the masses
Finally, we take the example of UAV videos, on which we test two depth algorithms that were initially tested on KITTI and show that the drone context is dramatically different from in-car videos.
End-to-end depth from motion with stabilized monocular videos
We propose a depth map inference system from monocular videos based on a novel dataset for navigation that mimics aerial footage from gimbal stabilized monocular camera in rigid scenes.
Learning structure-from-motion from motion
We then present results on a synthetic dataset that we believe to be more representative of typical UAV scenes.
Multi range Real-time depth inference from a monocular stabilized footage using a Fully Convolutional Neural Network
Using a neural network architecture for depth map inference from monocular stabilized videos with application to UAV videos in rigid scenes, we propose a multi-range architecture for unconstrained UAV flight, leveraging flight data from sensors to make accurate depth maps for uncluttered outdoor environment.
