Multi range Real-time depth inference from a monocular stabilized footage using a Fully Convolutional Neural Network
This work addresses depth estimation for UAV videos in uncluttered environments, but appears incremental as it builds on existing neural network methods with specific adaptations.
The paper tackles depth map inference from monocular stabilized videos, particularly for UAV applications in rigid outdoor scenes, and reports that their multi-range architecture improves depth inference with quantitative results on synthetic data.
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. We try our algorithm on both synthetic scenes and real UAV flight data. Quantitative results are given for synthetic scenes with a slightly noisy orientation, and show that our multi-range architecture improves depth inference. Along with this article is a video that present our results more thoroughly.