# Multi range Real-time depth inference from a monocular stabilized footage using a Fully Convolutional Neural Network

arXiv: https://arxiv.org/abs/1809.04467 · HTML: https://mlsanity.com/paper/1809.04467/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2018.

## Built on by (most cited first, 1 of 1)

- [Self-Supervised Monocular Depth Estimation of Untextured Indoor Rotated Scenes](https://mlsanity.com/paper/2106.12958/) (2021)

## Abstract (arXiv)

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.